wan2.2

wan/modules/animate/animate_utils.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import torch
+import numbers
+from peft import LoraConfig
+
+
+def get_loraconfig(transformer, rank=128, alpha=128, init_lora_weights="gaussian"):
+    target_modules = []
+    for name, module in transformer.named_modules():
+        if "blocks" in name and "face" not in name and "modulation" not in name and isinstance(module, torch.nn.Linear):
+            target_modules.append(name)
+
+    transformer_lora_config = LoraConfig(
+        r=rank,
+        lora_alpha=alpha,
+        init_lora_weights=init_lora_weights,
+        target_modules=target_modules,
+    )
+    return transformer_lora_config
+
+
+
+class TensorList(object):
+
+    def __init__(self, tensors):
+        """
+        tensors: a list of torch.Tensor objects. No need to have uniform shape.
+        """
+        assert isinstance(tensors, (list, tuple))
+        assert all(isinstance(u, torch.Tensor) for u in tensors)
+        assert len(set([u.ndim for u in tensors])) == 1
+        assert len(set([u.dtype for u in tensors])) == 1
+        assert len(set([u.device for u in tensors])) == 1
+        self.tensors = tensors
+    
+    def to(self, *args, **kwargs):
+        return TensorList([u.to(*args, **kwargs) for u in self.tensors])
+    
+    def size(self, dim):
+        assert dim == 0, 'only support get the 0th size'
+        return len(self.tensors)
+    
+    def pow(self, *args, **kwargs):
+        return TensorList([u.pow(*args, **kwargs) for u in self.tensors])
+    
+    def squeeze(self, dim):
+        assert dim != 0
+        if dim > 0:
+            dim -= 1
+        return TensorList([u.squeeze(dim) for u in self.tensors])
+    
+    def type(self, *args, **kwargs):
+        return TensorList([u.type(*args, **kwargs) for u in self.tensors])
+    
+    def type_as(self, other):
+        assert isinstance(other, (torch.Tensor, TensorList))
+        if isinstance(other, torch.Tensor):
+            return TensorList([u.type_as(other) for u in self.tensors])
+        else:
+            return TensorList([u.type(other.dtype) for u in self.tensors])
+    
+    @property
+    def dtype(self):
+        return self.tensors[0].dtype
+    
+    @property
+    def device(self):
+        return self.tensors[0].device
+    
+    @property
+    def ndim(self):
+        return 1 + self.tensors[0].ndim
+    
+    def __getitem__(self, index):
+        return self.tensors[index]
+    
+    def __len__(self):
+        return len(self.tensors)
+    
+    def __add__(self, other):
+        return self._apply(other, lambda u, v: u + v)
+    
+    def __radd__(self, other):
+        return self._apply(other, lambda u, v: v + u)
+    
+    def __sub__(self, other):
+        return self._apply(other, lambda u, v: u - v)
+    
+    def __rsub__(self, other):
+        return self._apply(other, lambda u, v: v - u)
+    
+    def __mul__(self, other):
+        return self._apply(other, lambda u, v: u * v)
+    
+    def __rmul__(self, other):
+        return self._apply(other, lambda u, v: v * u)
+    
+    def __floordiv__(self, other):
+        return self._apply(other, lambda u, v: u // v)
+    
+    def __truediv__(self, other):
+        return self._apply(other, lambda u, v: u / v)
+    
+    def __rfloordiv__(self, other):
+        return self._apply(other, lambda u, v: v // u)
+    
+    def __rtruediv__(self, other):
+        return self._apply(other, lambda u, v: v / u)
+    
+    def __pow__(self, other):
+        return self._apply(other, lambda u, v: u ** v)
+    
+    def __rpow__(self, other):
+        return self._apply(other, lambda u, v: v ** u)
+    
+    def __neg__(self):
+        return TensorList([-u for u in self.tensors])
+    
+    def __iter__(self):
+        for tensor in self.tensors:
+            yield tensor
+    
+    def __repr__(self):
+        return 'TensorList: \n' + repr(self.tensors)
+
+    def _apply(self, other, op):
+        if isinstance(other, (list, tuple, TensorList)) or (
+            isinstance(other, torch.Tensor) and (
+                other.numel() > 1 or other.ndim > 1
+            )
+        ):
+            assert len(other) == len(self.tensors)
+            return TensorList([op(u, v) for u, v in zip(self.tensors, other)])
+        elif isinstance(other, numbers.Number) or (
+            isinstance(other, torch.Tensor) and (
+                other.numel() == 1 and other.ndim <= 1
+            )
+        ):
+            return TensorList([op(u, other) for u in self.tensors])
+        else:
+            raise TypeError(
+                f'unsupported operand for *: "TensorList" and "{type(other)}"'
+            )
\ No newline at end of file

wan/modules/animate/clip.py

+# Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip''
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import logging
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+
+from ..attention import flash_attention
+from ..tokenizers import HuggingfaceTokenizer
+from .xlm_roberta import XLMRoberta
+
+__all__ = [
+    'XLMRobertaCLIP',
+    'clip_xlm_roberta_vit_h_14',
+    'CLIPModel',
+]
+
+
+def pos_interpolate(pos, seq_len):
+    if pos.size(1) == seq_len:
+        return pos
+    else:
+        src_grid = int(math.sqrt(pos.size(1)))
+        tar_grid = int(math.sqrt(seq_len))
+        n = pos.size(1) - src_grid * src_grid
+        return torch.cat([
+            pos[:, :n],
+            F.interpolate(
+                pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute(
+                    0, 3, 1, 2),
+                size=(tar_grid, tar_grid),
+                mode='bicubic',
+                align_corners=False).flatten(2).transpose(1, 2)
+        ],
+                         dim=1)
+
+
+class QuickGELU(nn.Module):
+
+    def forward(self, x):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class LayerNorm(nn.LayerNorm):
+
+    def forward(self, x):
+        return super().forward(x.float()).type_as(x)
+
+
+class SelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 causal=False,
+                 attn_dropout=0.0,
+                 proj_dropout=0.0):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.causal = causal
+        self.attn_dropout = attn_dropout
+        self.proj_dropout = proj_dropout
+
+        # layers
+        self.to_qkv = nn.Linear(dim, dim * 3)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x):
+        """
+        x:   [B, L, C].
+        """
+        b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2)
+
+        # compute attention
+        p = self.attn_dropout if self.training else 0.0
+        x = flash_attention(q, k, v, dropout_p=p, causal=self.causal, version=2)
+        x = x.reshape(b, s, c)
+
+        # output
+        x = self.proj(x)
+        x = F.dropout(x, self.proj_dropout, self.training)
+        return x
+
+
+class SwiGLU(nn.Module):
+
+    def __init__(self, dim, mid_dim):
+        super().__init__()
+        self.dim = dim
+        self.mid_dim = mid_dim
+
+        # layers
+        self.fc1 = nn.Linear(dim, mid_dim)
+        self.fc2 = nn.Linear(dim, mid_dim)
+        self.fc3 = nn.Linear(mid_dim, dim)
+
+    def forward(self, x):
+        x = F.silu(self.fc1(x)) * self.fc2(x)
+        x = self.fc3(x)
+        return x
+
+
+class AttentionBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 mlp_ratio,
+                 num_heads,
+                 post_norm=False,
+                 causal=False,
+                 activation='quick_gelu',
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 norm_eps=1e-5):
+        assert activation in ['quick_gelu', 'gelu', 'swi_glu']
+        super().__init__()
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.post_norm = post_norm
+        self.causal = causal
+        self.norm_eps = norm_eps
+
+        # layers
+        self.norm1 = LayerNorm(dim, eps=norm_eps)
+        self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
+                                  proj_dropout)
+        self.norm2 = LayerNorm(dim, eps=norm_eps)
+        if activation == 'swi_glu':
+            self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
+        else:
+            self.mlp = nn.Sequential(
+                nn.Linear(dim, int(dim * mlp_ratio)),
+                QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
+                nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
+
+    def forward(self, x):
+        if self.post_norm:
+            x = x + self.norm1(self.attn(x))
+            x = x + self.norm2(self.mlp(x))
+        else:
+            x = x + self.attn(self.norm1(x))
+            x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class AttentionPool(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 mlp_ratio,
+                 num_heads,
+                 activation='gelu',
+                 proj_dropout=0.0,
+                 norm_eps=1e-5):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.proj_dropout = proj_dropout
+        self.norm_eps = norm_eps
+
+        # layers
+        gain = 1.0 / math.sqrt(dim)
+        self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
+        self.to_q = nn.Linear(dim, dim)
+        self.to_kv = nn.Linear(dim, dim * 2)
+        self.proj = nn.Linear(dim, dim)
+        self.norm = LayerNorm(dim, eps=norm_eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, int(dim * mlp_ratio)),
+            QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
+            nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
+
+    def forward(self, x):
+        """
+        x:  [B, L, C].
+        """
+        b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.to_q(self.cls_embedding).view(1, 1, n, d).expand(b, -1, -1, -1)
+        k, v = self.to_kv(x).view(b, s, 2, n, d).unbind(2)
+
+        # compute attention
+        x = flash_attention(q, k, v, version=2)
+        x = x.reshape(b, 1, c)
+
+        # output
+        x = self.proj(x)
+        x = F.dropout(x, self.proj_dropout, self.training)
+
+        # mlp
+        x = x + self.mlp(self.norm(x))
+        return x[:, 0]
+
+
+class VisionTransformer(nn.Module):
+
+    def __init__(self,
+                 image_size=224,
+                 patch_size=16,
+                 dim=768,
+                 mlp_ratio=4,
+                 out_dim=512,
+                 num_heads=12,
+                 num_layers=12,
+                 pool_type='token',
+                 pre_norm=True,
+                 post_norm=False,
+                 activation='quick_gelu',
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 embedding_dropout=0.0,
+                 norm_eps=1e-5):
+        if image_size % patch_size != 0:
+            print(
+                '[WARNING] image_size is not divisible by patch_size',
+                flush=True)
+        assert pool_type in ('token', 'token_fc', 'attn_pool')
+        out_dim = out_dim or dim
+        super().__init__()
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = (image_size // patch_size)**2
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.out_dim = out_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.pool_type = pool_type
+        self.post_norm = post_norm
+        self.norm_eps = norm_eps
+
+        # embeddings
+        gain = 1.0 / math.sqrt(dim)
+        self.patch_embedding = nn.Conv2d(
+            3,
+            dim,
+            kernel_size=patch_size,
+            stride=patch_size,
+            bias=not pre_norm)
+        if pool_type in ('token', 'token_fc'):
+            self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
+        self.pos_embedding = nn.Parameter(gain * torch.randn(
+            1, self.num_patches +
+            (1 if pool_type in ('token', 'token_fc') else 0), dim))
+        self.dropout = nn.Dropout(embedding_dropout)
+
+        # transformer
+        self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
+        self.transformer = nn.Sequential(*[
+            AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False,
+                           activation, attn_dropout, proj_dropout, norm_eps)
+            for _ in range(num_layers)
+        ])
+        self.post_norm = LayerNorm(dim, eps=norm_eps)
+
+        # head
+        if pool_type == 'token':
+            self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
+        elif pool_type == 'token_fc':
+            self.head = nn.Linear(dim, out_dim)
+        elif pool_type == 'attn_pool':
+            self.head = AttentionPool(dim, mlp_ratio, num_heads, activation,
+                                      proj_dropout, norm_eps)
+
+    def forward(self, x, interpolation=False, use_31_block=False):
+        b = x.size(0)
+
+        # embeddings
+        x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
+        if self.pool_type in ('token', 'token_fc'):
+            x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1)
+        if interpolation:
+            e = pos_interpolate(self.pos_embedding, x.size(1))
+        else:
+            e = self.pos_embedding
+        x = self.dropout(x + e)
+        if self.pre_norm is not None:
+            x = self.pre_norm(x)
+
+        # transformer
+        if use_31_block:
+            x = self.transformer[:-1](x)
+            return x
+        else:
+            x = self.transformer(x)
+            return x
+
+
+class XLMRobertaWithHead(XLMRoberta):
+
+    def __init__(self, **kwargs):
+        self.out_dim = kwargs.pop('out_dim')
+        super().__init__(**kwargs)
+
+        # head
+        mid_dim = (self.dim + self.out_dim) // 2
+        self.head = nn.Sequential(
+            nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(),
+            nn.Linear(mid_dim, self.out_dim, bias=False))
+
+    def forward(self, ids):
+        # xlm-roberta
+        x = super().forward(ids)
+
+        # average pooling
+        mask = ids.ne(self.pad_id).unsqueeze(-1).to(x)
+        x = (x * mask).sum(dim=1) / mask.sum(dim=1)
+
+        # head
+        x = self.head(x)
+        return x
+
+
+class XLMRobertaCLIP(nn.Module):
+
+    def __init__(self,
+                 embed_dim=1024,
+                 image_size=224,
+                 patch_size=14,
+                 vision_dim=1280,
+                 vision_mlp_ratio=4,
+                 vision_heads=16,
+                 vision_layers=32,
+                 vision_pool='token',
+                 vision_pre_norm=True,
+                 vision_post_norm=False,
+                 activation='gelu',
+                 vocab_size=250002,
+                 max_text_len=514,
+                 type_size=1,
+                 pad_id=1,
+                 text_dim=1024,
+                 text_heads=16,
+                 text_layers=24,
+                 text_post_norm=True,
+                 text_dropout=0.1,
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 embedding_dropout=0.0,
+                 norm_eps=1e-5):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.vision_dim = vision_dim
+        self.vision_mlp_ratio = vision_mlp_ratio
+        self.vision_heads = vision_heads
+        self.vision_layers = vision_layers
+        self.vision_pre_norm = vision_pre_norm
+        self.vision_post_norm = vision_post_norm
+        self.activation = activation
+        self.vocab_size = vocab_size
+        self.max_text_len = max_text_len
+        self.type_size = type_size
+        self.pad_id = pad_id
+        self.text_dim = text_dim
+        self.text_heads = text_heads
+        self.text_layers = text_layers
+        self.text_post_norm = text_post_norm
+        self.norm_eps = norm_eps
+
+        # models
+        self.visual = VisionTransformer(
+            image_size=image_size,
+            patch_size=patch_size,
+            dim=vision_dim,
+            mlp_ratio=vision_mlp_ratio,
+            out_dim=embed_dim,
+            num_heads=vision_heads,
+            num_layers=vision_layers,
+            pool_type=vision_pool,
+            pre_norm=vision_pre_norm,
+            post_norm=vision_post_norm,
+            activation=activation,
+            attn_dropout=attn_dropout,
+            proj_dropout=proj_dropout,
+            embedding_dropout=embedding_dropout,
+            norm_eps=norm_eps)
+        self.textual = XLMRobertaWithHead(
+            vocab_size=vocab_size,
+            max_seq_len=max_text_len,
+            type_size=type_size,
+            pad_id=pad_id,
+            dim=text_dim,
+            out_dim=embed_dim,
+            num_heads=text_heads,
+            num_layers=text_layers,
+            post_norm=text_post_norm,
+            dropout=text_dropout)
+        self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
+
+    def forward(self, imgs, txt_ids):
+        """
+        imgs:       [B, 3, H, W] of torch.float32.
+        - mean:     [0.48145466, 0.4578275, 0.40821073]
+        - std:      [0.26862954, 0.26130258, 0.27577711]
+        txt_ids:    [B, L] of torch.long.
+                    Encoded by data.CLIPTokenizer.
+        """
+        xi = self.visual(imgs)
+        xt = self.textual(txt_ids)
+        return xi, xt
+
+    def param_groups(self):
+        groups = [{
+            'params': [
+                p for n, p in self.named_parameters()
+                if 'norm' in n or n.endswith('bias')
+            ],
+            'weight_decay': 0.0
+        }, {
+            'params': [
+                p for n, p in self.named_parameters()
+                if not ('norm' in n or n.endswith('bias'))
+            ]
+        }]
+        return groups
+
+
+def _clip(pretrained=False,
+          pretrained_name=None,
+          model_cls=XLMRobertaCLIP,
+          return_transforms=False,
+          return_tokenizer=False,
+          tokenizer_padding='eos',
+          dtype=torch.float32,
+          device='cpu',
+          **kwargs):
+    # init a model on device
+    with torch.device(device):
+        model = model_cls(**kwargs)
+
+    # set device
+    model = model.to(dtype=dtype, device=device)
+    output = (model,)
+
+    # init transforms
+    if return_transforms:
+        # mean and std
+        if 'siglip' in pretrained_name.lower():
+            mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
+        else:
+            mean = [0.48145466, 0.4578275, 0.40821073]
+            std = [0.26862954, 0.26130258, 0.27577711]
+
+        # transforms
+        transforms = T.Compose([
+            T.Resize((model.image_size, model.image_size),
+                     interpolation=T.InterpolationMode.BICUBIC),
+            T.ToTensor(),
+            T.Normalize(mean=mean, std=std)
+        ])
+        output += (transforms,)
+    return output[0] if len(output) == 1 else output
+
+
+def clip_xlm_roberta_vit_h_14(
+        pretrained=False,
+        pretrained_name='open-clip-xlm-roberta-large-vit-huge-14',
+        **kwargs):
+    cfg = dict(
+        embed_dim=1024,
+        image_size=224,
+        patch_size=14,
+        vision_dim=1280,
+        vision_mlp_ratio=4,
+        vision_heads=16,
+        vision_layers=32,
+        vision_pool='token',
+        activation='gelu',
+        vocab_size=250002,
+        max_text_len=514,
+        type_size=1,
+        pad_id=1,
+        text_dim=1024,
+        text_heads=16,
+        text_layers=24,
+        text_post_norm=True,
+        text_dropout=0.1,
+        attn_dropout=0.0,
+        proj_dropout=0.0,
+        embedding_dropout=0.0)
+    cfg.update(**kwargs)
+    return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg)
+
+
+class CLIPModel:
+
+    def __init__(self, dtype, device, checkpoint_path, tokenizer_path):
+        self.dtype = dtype
+        self.device = device
+        self.checkpoint_path = checkpoint_path
+        self.tokenizer_path = tokenizer_path
+
+        # init model
+        self.model, self.transforms = clip_xlm_roberta_vit_h_14(
+            pretrained=False,
+            return_transforms=True,
+            return_tokenizer=False,
+            dtype=dtype,
+            device=device)
+        self.model = self.model.eval().requires_grad_(False)
+        logging.info(f'loading {checkpoint_path}')
+        self.model.load_state_dict(
+            torch.load(checkpoint_path, map_location='cpu'))
+
+        # init tokenizer
+        self.tokenizer = HuggingfaceTokenizer(
+            name=tokenizer_path,
+            seq_len=self.model.max_text_len - 2,
+            clean='whitespace')
+
+    def visual(self, videos):
+        # preprocess
+        size = (self.model.image_size,) * 2
+        videos = torch.cat([
+            F.interpolate(
+                u.transpose(0, 1),
+                size=size,
+                mode='bicubic',
+                align_corners=False) for u in videos
+        ])
+        videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
+
+        # forward
+        with torch.cuda.amp.autocast(dtype=self.dtype):
+            out = self.model.visual(videos, use_31_block=True)
+            return out
\ No newline at end of file

wan/modules/animate/face_blocks.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+from torch import nn
+import torch
+from typing import Tuple, Optional
+from einops import rearrange
+import torch.nn.functional as F
+import math
+from ...distributed.util import gather_forward, get_rank, get_world_size
+
+
+try:
+    from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
+except ImportError:
+    flash_attn_func = None
+
+MEMORY_LAYOUT = {
+    "flash": (
+        lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]),
+        lambda x: x,
+    ),
+    "torch": (
+        lambda x: x.transpose(1, 2),
+        lambda x: x.transpose(1, 2),
+    ),
+    "vanilla": (
+        lambda x: x.transpose(1, 2),
+        lambda x: x.transpose(1, 2),
+    ),
+}
+
+
+def attention(
+    q,
+    k,
+    v,
+    mode="flash",
+    drop_rate=0,
+    attn_mask=None,
+    causal=False,
+    max_seqlen_q=None,
+    batch_size=1,
+):
+    """
+    Perform QKV self attention.
+
+    Args:
+        q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads.
+        k (torch.Tensor): Key tensor with shape [b, s1, a, d]
+        v (torch.Tensor): Value tensor with shape [b, s1, a, d]
+        mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'.
+        drop_rate (float): Dropout rate in attention map. (default: 0)
+        attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla).
+            (default: None)
+        causal (bool): Whether to use causal attention. (default: False)
+        cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
+            used to index into q.
+        cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
+            used to index into kv.
+        max_seqlen_q (int): The maximum sequence length in the batch of q.
+        max_seqlen_kv (int): The maximum sequence length in the batch of k and v.
+
+    Returns:
+        torch.Tensor: Output tensor after self attention with shape [b, s, ad]
+    """
+    pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode]
+
+    if mode == "torch":
+        if attn_mask is not None and attn_mask.dtype != torch.bool:
+            attn_mask = attn_mask.to(q.dtype)
+        x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal)
+
+    elif mode == "flash":
+        x = flash_attn_func(
+            q,
+            k,
+            v,
+        )
+        x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1])  # reshape x to [b, s, a, d]
+    elif mode == "vanilla":
+        scale_factor = 1 / math.sqrt(q.size(-1))
+
+        b, a, s, _ = q.shape
+        s1 = k.size(2)
+        attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device)
+        if causal:
+            # Only applied to self attention
+            assert attn_mask is None, "Causal mask and attn_mask cannot be used together"
+            temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril(diagonal=0)
+            attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
+            attn_bias.to(q.dtype)
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.bool:
+                attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
+            else:
+                attn_bias += attn_mask
+
+        attn = (q @ k.transpose(-2, -1)) * scale_factor
+        attn += attn_bias
+        attn = attn.softmax(dim=-1)
+        attn = torch.dropout(attn, p=drop_rate, train=True)
+        x = attn @ v
+    else:
+        raise NotImplementedError(f"Unsupported attention mode: {mode}")
+
+    x = post_attn_layout(x)
+    b, s, a, d = x.shape
+    out = x.reshape(b, s, -1)
+    return out
+
+
+class CausalConv1d(nn.Module):
+
+    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", **kwargs):
+        super().__init__()
+
+        self.pad_mode = pad_mode
+        padding = (kernel_size - 1, 0)  # T
+        self.time_causal_padding = padding
+
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+
+class FaceEncoder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1)
+        self.norm1 = nn.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.act = nn.SiLU()
+        self.conv2 = CausalConv1d(1024, 1024, 3, stride=2)
+        self.conv3 = CausalConv1d(1024, 1024, 3, stride=2)
+
+        self.out_proj = nn.Linear(1024, hidden_dim)
+        self.norm1 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm2 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm3 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim))
+
+    def forward(self, x):
+        
+        x = rearrange(x, "b t c -> b c t")
+        b, c, t = x.shape
+
+        x = self.conv1_local(x)
+        x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
+        
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv2(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv3(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm3(x)
+        x = self.act(x)
+        x = self.out_proj(x)
+        x = rearrange(x, "(b n) t c -> b t n c", b=b)
+        padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1)
+        x = torch.cat([x, padding], dim=-2)
+        x_local = x.clone()
+
+        return x_local
+
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+
+
+def get_norm_layer(norm_layer):
+    """
+    Get the normalization layer.
+
+    Args:
+        norm_layer (str): The type of normalization layer.
+
+    Returns:
+        norm_layer (nn.Module): The normalization layer.
+    """
+    if norm_layer == "layer":
+        return nn.LayerNorm
+    elif norm_layer == "rms":
+        return RMSNorm
+    else:
+        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+
+
+class FaceAdapter(nn.Module):
+    def __init__(
+        self,
+        hidden_dim: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        num_adapter_layers: int = 1,
+        dtype=None,
+        device=None,
+    ):
+
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.hidden_size = hidden_dim
+        self.heads_num = heads_num
+        self.fuser_blocks = nn.ModuleList(
+            [
+                FaceBlock(
+                    self.hidden_size,
+                    self.heads_num,
+                    qk_norm=qk_norm,
+                    qk_norm_type=qk_norm_type,
+                    **factory_kwargs,
+                )
+                for _ in range(num_adapter_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_embed: torch.Tensor,
+        idx: int,
+        freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
+        freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
+
+
+
+class FaceBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        qk_scale: float = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+
+        self.deterministic = False
+        self.hidden_size = hidden_size
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+        self.scale = qk_scale or head_dim**-0.5
+       
+        self.linear1_kv = nn.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
+        self.linear1_q = nn.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        self.linear2 = nn.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        qk_norm_layer = get_norm_layer(qk_norm_type)
+        self.q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+        self.k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+
+        self.pre_norm_feat = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.pre_norm_motion = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_vec: torch.Tensor,
+        motion_mask: Optional[torch.Tensor] = None,
+        use_context_parallel=False,
+    ) -> torch.Tensor:
+        
+        B, T, N, C = motion_vec.shape
+        T_comp = T
+
+        x_motion = self.pre_norm_motion(motion_vec)
+        x_feat = self.pre_norm_feat(x)
+
+        kv = self.linear1_kv(x_motion)
+        q = self.linear1_q(x_feat)
+
+        k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
+        q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
+
+        # Apply QK-Norm if needed.
+        q = self.q_norm(q).to(v)
+        k = self.k_norm(k).to(v)
+
+        k = rearrange(k, "B L N H D -> (B L) N H D")  
+        v = rearrange(v, "B L N H D -> (B L) N H D") 
+
+        if use_context_parallel:
+            q = gather_forward(q, dim=1)
+
+        q = rearrange(q, "B (L S) H D -> (B L) S H D", L=T_comp)  
+        # Compute attention.
+        attn = attention(
+            q,
+            k,
+            v,
+            max_seqlen_q=q.shape[1],
+            batch_size=q.shape[0],
+        )
+
+        attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)
+        if use_context_parallel:
+            attn = torch.chunk(attn, get_world_size(), dim=1)[get_rank()]
+
+        output = self.linear2(attn)
+
+        if motion_mask is not None:
+            output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
+
+        return output
\ No newline at end of file

wan/modules/animate/model_animate.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import math
+import types
+from copy import deepcopy
+from einops import  rearrange
+from typing import List
+import numpy as np
+import torch
+import torch.cuda.amp as amp
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.loaders import PeftAdapterMixin
+
+from ...distributed.sequence_parallel import (
+    distributed_attention,
+    gather_forward,
+    get_rank,
+    get_world_size,
+)
+
+
+from ..model import (
+    Head,
+    WanAttentionBlock,
+    WanLayerNorm,
+    WanRMSNorm,
+    WanModel,
+    WanSelfAttention,
+    flash_attention,
+    rope_params,
+    sinusoidal_embedding_1d,
+    rope_apply
+)
+
+from .face_blocks import FaceEncoder, FaceAdapter
+from .motion_encoder import Generator
+
+class HeadAnimate(Head):
+
+    def forward(self, x, e):
+        """
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            e(Tensor): Shape [B, L1, C]
+        """
+        assert e.dtype == torch.float32
+        with amp.autocast(dtype=torch.float32):
+            e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
+            x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
+        return x
+
+
+class WanAnimateSelfAttention(WanSelfAttention):
+
+    def forward(self, x, seq_lens, grid_sizes, freqs):
+        """
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            seq_lens(Tensor): Shape [B]
+            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+        # query, key, value function
+        def qkv_fn(x):
+            q = self.norm_q(self.q(x)).view(b, s, n, d)
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            v = self.v(x).view(b, s, n, d)
+            return q, k, v
+
+        q, k, v = qkv_fn(x)
+
+        x = flash_attention(
+            q=rope_apply(q, grid_sizes, freqs),
+            k=rope_apply(k, grid_sizes, freqs),
+            v=v,
+            k_lens=seq_lens,
+            window_size=self.window_size)
+
+        # output
+        x = x.flatten(2)
+        x = self.o(x)
+        return x
+
+
+class WanAnimateCrossAttention(WanSelfAttention):
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        window_size=(-1, -1),
+        qk_norm=True,
+        eps=1e-6,
+        use_img_emb=True
+    ):
+        super().__init__(
+            dim,
+            num_heads,
+            window_size,
+            qk_norm,
+            eps
+        )
+        self.use_img_emb = use_img_emb
+
+        if use_img_emb:
+            self.k_img = nn.Linear(dim, dim)
+            self.v_img = nn.Linear(dim, dim)
+            self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
+
+    def forward(self, x, context, context_lens):
+        """
+        x:              [B, L1, C].
+        context:        [B, L2, C].
+        context_lens:   [B].
+        """
+        if self.use_img_emb:
+            context_img = context[:, :257]
+            context = context[:, 257:]
+        else:
+            context = context
+
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.norm_q(self.q(x)).view(b, -1, n, d)
+        k = self.norm_k(self.k(context)).view(b, -1, n, d)
+        v = self.v(context).view(b, -1, n, d)
+
+        if self.use_img_emb:
+            k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
+            v_img = self.v_img(context_img).view(b, -1, n, d)
+            img_x = flash_attention(q, k_img, v_img, k_lens=None)
+        # compute attention
+        x = flash_attention(q, k, v, k_lens=context_lens)
+
+        # output
+        x = x.flatten(2)
+
+        if self.use_img_emb:
+            img_x = img_x.flatten(2)
+            x = x + img_x
+
+        x = self.o(x)
+        return x
+
+
+class WanAnimateAttentionBlock(nn.Module):
+    def __init__(self,
+                 dim,
+                 ffn_dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 use_img_emb=True):
+
+        super().__init__()
+        self.dim = dim
+        self.ffn_dim = ffn_dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps = eps
+
+        # layers
+        self.norm1 = WanLayerNorm(dim, eps)
+        self.self_attn = WanAnimateSelfAttention(dim, num_heads, window_size, qk_norm, eps)
+        
+        self.norm3 = WanLayerNorm(
+            dim, eps, elementwise_affine=True
+        ) if cross_attn_norm else nn.Identity()
+            
+        self.cross_attn = WanAnimateCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps, use_img_emb=use_img_emb)
+        self.norm2 = WanLayerNorm(dim, eps)
+        self.ffn = nn.Sequential(
+            nn.Linear(dim, ffn_dim),
+            nn.GELU(approximate='tanh'),
+            nn.Linear(ffn_dim, dim)
+        )
+
+        # modulation
+        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim ** 0.5)
+
+    def forward(
+        self,
+        x,
+        e,
+        seq_lens,
+        grid_sizes,
+        freqs,
+        context,
+        context_lens,
+    ):
+        """
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, L1, 6, C]
+            seq_lens(Tensor): Shape [B], length of each sequence in batch
+            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        assert e.dtype == torch.float32
+        with amp.autocast(dtype=torch.float32):
+            e = (self.modulation + e).chunk(6, dim=1)
+        assert e[0].dtype == torch.float32
+
+        # self-attention
+        y = self.self_attn(
+            self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes, freqs
+        )
+        with amp.autocast(dtype=torch.float32):
+            x = x + y * e[2]
+
+        # cross-attention & ffn function
+        def cross_attn_ffn(x, context, context_lens, e):
+            x = x + self.cross_attn(self.norm3(x), context, context_lens)
+            y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3])
+            with amp.autocast(dtype=torch.float32):
+                x = x + y * e[5]
+            return x
+
+        x = cross_attn_ffn(x, context, context_lens, e)
+        return x
+
+
+class MLPProj(torch.nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            torch.nn.LayerNorm(in_dim),
+            torch.nn.Linear(in_dim, in_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(in_dim, out_dim),
+            torch.nn.LayerNorm(out_dim),
+        )
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+class WanAnimateModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _no_split_modules = ['WanAttentionBlock']
+
+    @register_to_config
+    def __init__(self,
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=36,
+                 dim=5120,
+                 ffn_dim=13824,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=40,
+                 num_layers=40,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 motion_encoder_dim=512,
+                 use_context_parallel=False,
+                 use_img_emb=True):
+
+        super().__init__()
+        self.patch_size = patch_size
+        self.text_len = text_len
+        self.in_dim = in_dim
+        self.dim = dim
+        self.ffn_dim = ffn_dim
+        self.freq_dim = freq_dim
+        self.text_dim = text_dim
+        self.out_dim = out_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps = eps
+        self.motion_encoder_dim = motion_encoder_dim
+        self.use_context_parallel = use_context_parallel
+        self.use_img_emb = use_img_emb
+
+        # embeddings
+        self.patch_embedding = nn.Conv3d(
+            in_dim, dim, kernel_size=patch_size, stride=patch_size)
+
+        self.pose_patch_embedding = nn.Conv3d(
+            16, dim, kernel_size=patch_size, stride=patch_size
+        )
+
+        self.text_embedding = nn.Sequential(
+            nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
+            nn.Linear(dim, dim))
+
+        self.time_embedding = nn.Sequential(
+            nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
+
+        # blocks
+        self.blocks = nn.ModuleList([
+            WanAnimateAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm,
+                              cross_attn_norm, eps, use_img_emb) for _ in range(num_layers)
+        ])
+
+        # head
+        self.head = HeadAnimate(dim, out_dim, patch_size, eps)
+
+        # buffers (don't use register_buffer otherwise dtype will be changed in to())
+        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
+        d = dim // num_heads
+        self.freqs = torch.cat([
+            rope_params(1024, d - 4 * (d // 6)),
+            rope_params(1024, 2 * (d // 6)),
+            rope_params(1024, 2 * (d // 6))
+        ], dim=1)
+
+        self.img_emb = MLPProj(1280, dim)
+        
+        # initialize weights
+        self.init_weights()
+
+        self.motion_encoder = Generator(size=512, style_dim=512, motion_dim=20)
+        self.face_adapter = FaceAdapter(
+            heads_num=self.num_heads,
+            hidden_dim=self.dim,
+            num_adapter_layers=self.num_layers // 5,
+        )
+
+        self.face_encoder = FaceEncoder(
+            in_dim=motion_encoder_dim,
+            hidden_dim=self.dim,
+            num_heads=4,
+        )
+
+    def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, face_pixel_values):
+        pose_latents = [self.pose_patch_embedding(u.unsqueeze(0)) for u in pose_latents]
+        for x_, pose_latents_ in zip(x, pose_latents):
+            x_[:, :, 1:] += pose_latents_
+        
+        b,c,T,h,w = face_pixel_values.shape
+        face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
+
+        encode_bs = 8
+        face_pixel_values_tmp = []
+        for i in range(math.ceil(face_pixel_values.shape[0]/encode_bs)):
+            face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i*encode_bs:(i+1)*encode_bs]))
+
+        motion_vec = torch.cat(face_pixel_values_tmp)
+        
+        motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
+        motion_vec = self.face_encoder(motion_vec)
+
+        B, L, H, C = motion_vec.shape
+        pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
+        motion_vec = torch.cat([pad_face, motion_vec], dim=1)
+        return x, motion_vec
+
+
+    def after_transformer_block(self, block_idx, x, motion_vec, motion_masks=None):
+        if block_idx % 5 == 0:
+            adapter_args = [x, motion_vec, motion_masks, self.use_context_parallel]
+            residual_out = self.face_adapter.fuser_blocks[block_idx // 5](*adapter_args)
+            x = residual_out + x
+        return x
+
+
+    def forward(
+        self,
+        x,
+        t,
+        clip_fea,
+        context,
+        seq_len,
+        y=None,
+        pose_latents=None, 
+        face_pixel_values=None
+    ):
+        # params
+        device = self.patch_embedding.weight.device
+        if self.freqs.device != device:
+            self.freqs = self.freqs.to(device)
+
+        if y is not None:
+            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
+
+        # embeddings
+        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
+        x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
+
+        grid_sizes = torch.stack(
+            [torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
+        x = [u.flatten(2).transpose(1, 2) for u in x]
+        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
+        assert seq_lens.max() <= seq_len
+        x = torch.cat([
+            torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
+                      dim=1) for u in x
+        ])
+
+        # time embeddings
+        with amp.autocast(dtype=torch.float32):
+            e = self.time_embedding(
+                sinusoidal_embedding_1d(self.freq_dim, t).float()
+            )
+            e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+            assert e.dtype == torch.float32 and e0.dtype == torch.float32
+
+        # context
+        context_lens = None
+        context = self.text_embedding(
+            torch.stack([
+                torch.cat(
+                    [u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
+                for u in context
+            ]))
+
+        if self.use_img_emb:
+            context_clip = self.img_emb(clip_fea) # bs x 257 x dim
+            context = torch.concat([context_clip, context], dim=1)
+
+        # arguments
+        kwargs = dict(
+            e=e0,
+            seq_lens=seq_lens,
+            grid_sizes=grid_sizes,
+            freqs=self.freqs,
+            context=context,
+            context_lens=context_lens)
+
+        if self.use_context_parallel:
+            x = torch.chunk(x, get_world_size(), dim=1)[get_rank()]
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x, **kwargs)
+            x = self.after_transformer_block(idx, x, motion_vec)
+
+        # head
+        x = self.head(x, e)
+
+        if self.use_context_parallel:
+            x = gather_forward(x, dim=1)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return [u.float() for u in x]
+
+
+    def unpatchify(self, x, grid_sizes):
+        r"""
+        Reconstruct video tensors from patch embeddings.
+
+        Args:
+            x (List[Tensor]):
+                List of patchified features, each with shape [L, C_out * prod(patch_size)]
+            grid_sizes (Tensor):
+                Original spatial-temporal grid dimensions before patching,
+                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
+
+        Returns:
+            List[Tensor]:
+                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
+        """
+
+        c = self.out_dim
+        out = []
+        for u, v in zip(x, grid_sizes.tolist()):
+            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
+            u = torch.einsum('fhwpqrc->cfphqwr', u)
+            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
+            out.append(u)
+        return out
+
+    def init_weights(self):
+        r"""
+        Initialize model parameters using Xavier initialization.
+        """
+
+        # basic init
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight)
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+
+        # init embeddings
+        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
+        for m in self.text_embedding.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, std=.02)
+        for m in self.time_embedding.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, std=.02)
+
+        # init output layer
+        nn.init.zeros_(self.head.head.weight)

wan/modules/animate/motion_encoder.py

+# Modified from ``https://github.com/wyhsirius/LIA``
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+
+def custom_qr(input_tensor):
+    original_dtype = input_tensor.dtype
+    if original_dtype == torch.bfloat16:
+        q, r = torch.linalg.qr(input_tensor.to(torch.float32))
+        return q.to(original_dtype), r.to(original_dtype)
+    return torch.linalg.qr(input_tensor)
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+	return F.leaky_relu(input + bias, negative_slope) * scale
+
+
+def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
+	_, minor, in_h, in_w = input.shape
+	kernel_h, kernel_w = kernel.shape
+
+	out = input.view(-1, minor, in_h, 1, in_w, 1)
+	out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
+	out = out.view(-1, minor, in_h * up_y, in_w * up_x)
+
+	out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
+	out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
+		  max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ]
+
+	out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
+	w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+	out = F.conv2d(out, w)
+	out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+					  in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, )
+	return out[:, :, ::down_y, ::down_x]
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+	return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+
+
+def make_kernel(k):
+	k = torch.tensor(k, dtype=torch.float32)
+	if k.ndim == 1:
+		k = k[None, :] * k[:, None]
+	k /= k.sum()
+	return k
+
+
+class FusedLeakyReLU(nn.Module):
+	def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
+		super().__init__()
+		self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
+		self.negative_slope = negative_slope
+		self.scale = scale
+
+	def forward(self, input):
+		out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+		return out
+
+
+class Blur(nn.Module):
+	def __init__(self, kernel, pad, upsample_factor=1):
+		super().__init__()
+
+		kernel = make_kernel(kernel)
+
+		if upsample_factor > 1:
+			kernel = kernel * (upsample_factor ** 2)
+
+		self.register_buffer('kernel', kernel)
+
+		self.pad = pad
+
+	def forward(self, input):
+		return upfirdn2d(input, self.kernel, pad=self.pad)
+
+
+class ScaledLeakyReLU(nn.Module):
+	def __init__(self, negative_slope=0.2):
+		super().__init__()
+
+		self.negative_slope = negative_slope
+
+	def forward(self, input):
+		return F.leaky_relu(input, negative_slope=self.negative_slope)
+
+
+class EqualConv2d(nn.Module):
+	def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
+		super().__init__()
+
+		self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size))
+		self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+		self.stride = stride
+		self.padding = padding
+
+		if bias:
+			self.bias = nn.Parameter(torch.zeros(out_channel))
+		else:
+			self.bias = None
+
+	def forward(self, input):
+
+		return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding)
+
+	def __repr__(self):
+		return (
+			f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
+			f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
+		)
+
+
+class EqualLinear(nn.Module):
+	def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
+		super().__init__()
+
+		self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+		if bias:
+			self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+		else:
+			self.bias = None
+
+		self.activation = activation
+
+		self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+		self.lr_mul = lr_mul
+
+	def forward(self, input):
+
+		if self.activation:
+			out = F.linear(input, self.weight * self.scale)
+			out = fused_leaky_relu(out, self.bias * self.lr_mul)
+		else:
+			out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
+
+		return out
+
+	def __repr__(self):
+		return (f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})')
+
+
+class ConvLayer(nn.Sequential):
+	def __init__(
+			self,
+			in_channel,
+			out_channel,
+			kernel_size,
+			downsample=False,
+			blur_kernel=[1, 3, 3, 1],
+			bias=True,
+			activate=True,
+	):
+		layers = []
+
+		if downsample:
+			factor = 2
+			p = (len(blur_kernel) - factor) + (kernel_size - 1)
+			pad0 = (p + 1) // 2
+			pad1 = p // 2
+
+			layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+			stride = 2
+			self.padding = 0
+
+		else:
+			stride = 1
+			self.padding = kernel_size // 2
+
+		layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride,
+								  bias=bias and not activate))
+
+		if activate:
+			if bias:
+				layers.append(FusedLeakyReLU(out_channel))
+			else:
+				layers.append(ScaledLeakyReLU(0.2))
+
+		super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+	def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+		super().__init__()
+
+		self.conv1 = ConvLayer(in_channel, in_channel, 3)
+		self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+		self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False)
+
+	def forward(self, input):
+		out = self.conv1(input)
+		out = self.conv2(out)
+
+		skip = self.skip(input)
+		out = (out + skip) / math.sqrt(2)
+
+		return out
+
+
+class EncoderApp(nn.Module):
+	def __init__(self, size, w_dim=512):
+		super(EncoderApp, self).__init__()
+
+		channels = {
+			4: 512,
+			8: 512,
+			16: 512,
+			32: 512,
+			64: 256,
+			128: 128,
+			256: 64,
+			512: 32,
+			1024: 16
+		}
+
+		self.w_dim = w_dim
+		log_size = int(math.log(size, 2))
+
+		self.convs = nn.ModuleList()
+		self.convs.append(ConvLayer(3, channels[size], 1))
+
+		in_channel = channels[size]
+		for i in range(log_size, 2, -1):
+			out_channel = channels[2 ** (i - 1)]
+			self.convs.append(ResBlock(in_channel, out_channel))
+			in_channel = out_channel
+
+		self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False))
+
+	def forward(self, x):
+
+		res = []
+		h = x
+		for conv in self.convs:
+			h = conv(h)
+			res.append(h)
+
+		return res[-1].squeeze(-1).squeeze(-1), res[::-1][2:]
+
+
+class Encoder(nn.Module):
+	def __init__(self, size, dim=512, dim_motion=20):
+		super(Encoder, self).__init__()
+
+		# appearance netmork
+		self.net_app = EncoderApp(size, dim)
+
+		# motion network
+		fc = [EqualLinear(dim, dim)]
+		for i in range(3):
+			fc.append(EqualLinear(dim, dim))
+
+		fc.append(EqualLinear(dim, dim_motion))
+		self.fc = nn.Sequential(*fc)
+
+	def enc_app(self, x):
+		h_source = self.net_app(x)
+		return h_source
+
+	def enc_motion(self, x):
+		h, _ = self.net_app(x)
+		h_motion = self.fc(h)
+		return h_motion
+
+
+class Direction(nn.Module):
+    def __init__(self, motion_dim):
+        super(Direction, self).__init__()
+        self.weight = nn.Parameter(torch.randn(512, motion_dim))
+
+    def forward(self, input):
+
+        weight = self.weight + 1e-8
+        Q, R = custom_qr(weight)
+        if input is None:
+            return Q
+        else:
+            input_diag = torch.diag_embed(input)  # alpha, diagonal matrix
+            out = torch.matmul(input_diag, Q.T)
+            out = torch.sum(out, dim=1)
+            return out
+
+
+class Synthesis(nn.Module):
+    def __init__(self, motion_dim):
+        super(Synthesis, self).__init__()
+        self.direction = Direction(motion_dim)
+
+
+class Generator(nn.Module):
+    def __init__(self, size, style_dim=512, motion_dim=20):
+        super().__init__()
+
+        self.enc = Encoder(size, style_dim, motion_dim)
+        self.dec = Synthesis(motion_dim)
+
+    def get_motion(self, img):
+        #motion_feat = self.enc.enc_motion(img)
+        motion_feat = torch.utils.checkpoint.checkpoint((self.enc.enc_motion), img, use_reentrant=True)
+        with torch.cuda.amp.autocast(dtype=torch.float32):
+            motion = self.dec.direction(motion_feat)
+        return motion
\ No newline at end of file

wan/modules/animate/preprocess/UserGuider.md

+# Wan-animate Preprocessing User Guider
+
+## 1. Introductions
+
+
+Wan-animate offers two generation modes: `animation` and `replacement`. While both modes extract the skeleton from the reference video, they each have a distinct preprocessing pipeline.
+
+### 1.1 Animation Mode
+
+In this mode, it is highly recommended to enable pose retargeting, especially if the body proportions of the reference and driving characters are dissimilar.
+
+ - A simplified version of pose retargeting pipeline is provided to help developers quickly implement this functionality.
+
+ - **NOTE:** Due to the potential complexity of input data, the results from this simplified retargeting version are NOT guaranteed to be perfect. It is strongly advised to verify the preprocessing results before proceeding.
+
+ - Community contributions to improve on this feature are welcome.
+
+### 1.2 Replacement Mode
+
+ - Pose retargeting is DISABLED by default in this mode. This is a deliberate choice to account for potential spatial interactions between the character and the environment.
+
+ - **WARNING**: If there is a significant mismatch in body proportions between the reference and driving characters, artifacts or deformations may appear in the final output.
+
+ - A simplified version for extracting the character's mask is also provided.
+ - **WARNING:** This mask extraction process is designed for **single-person videos ONLY** and may produce incorrect results or fail in multi-person videos (incorrect pose tracking). For multi-person video, users are required to either develop their own solution or integrate a suitable open-source tool.
+
+---
+
+## 2. Preprocessing Instructions and Recommendations
+
+### 2.1 Basic Usage
+
+- The preprocessing process requires some additional models, including pose detection (mandatory), and mask extraction and image editing models (optional, as needed). Place them according to the following directory structure:
+```
+    /path/to/your/ckpt_path/
+    ├── det/
+    │ └── yolov10m.onnx
+    ├── pose2d/
+    │ └── vitpose_h_wholebody.onnx
+    ├── sam2/
+    │ └── sam2_hiera_large.pt
+    └── FLUX.1-Kontext-dev/
+```
+- `video_path`, `refer_path`, and `save_path` correspond to the paths for the input driving video, the character image, and the preprocessed results.
+
+- When using `animation` mode, two videos, `src_face.mp4` and `src_pose.mp4`, will be generated in `save_path`. When using `replacement` mode, two additional videos, `src_bg.mp4` and `src_mask.mp4`, will also be generated.
+
+- The `resolution_area` parameter determines the resolution for both preprocessing and the generation model. Its size is determined by pixel area.
+
+- The `fps` parameter can specify the frame rate for video processing. A lower frame rate can improve generation efficiency, but may cause stuttering or choppiness.
+
+---
+
+### 2.2 Animation Mode
+
+- We support three forms: not using pose retargeting, using basic pose retargeting, and using enhanced pose retargeting based on the `FLUX.1-Kontext-dev` image editing model. These are specified via the `retarget_flag` and `use_flux` parameters.
+
+- Specifying `retarget_flag` to use basic pose retargeting requires ensuring that both the reference character and the character in the first frame of the driving video are in a front-facing, stretched pose.
+
+- Other than that, we recommend using enhanced pose retargeting by specifying both `retarget_flag` and `use_flux`. **NOTE:** Due to the limited capabilities of `FLUX.1-Kontext-dev`, it is NOT guaranteed to produce the expected results (e.g., consistency is not maintained, the pose is incorrect, etc.). It is recommended to check the intermediate results as well as the finally generated pose video; both are stored in `save_path`. Of course, users can also use a better image editing model, or explore the prompts for Flux on their own.
+
+---
+
+### 2.3 Replacement Mode
+
+- Specifying `replace_flag` to enable data preprocessing for this mode. The preprocessing will additionally process a mask for the character in the video, and its size and shape can be adjusted by specifying some parameters.
+- `iterations` and `k` can make the mask larger, covering more area.
+- `w_len` and `h_len` can adjust the mask's shape. Smaller values will make the outline coarser, while larger values will make it finer.
+
+- A smaller, finer-contoured mask can allow for more of the original background to be preserved, but may potentially limit the character's generation area (considering potential appearance differences, this can lead to some shape leakage). A larger, coarser mask can allow the character generation to be more flexible and consistent, but because it includes more of the background, it might affect the background's consistency. We recommend users to adjust the relevant parameters based on their specific input data.
\ No newline at end of file

wan/modules/animate/preprocess/__init__.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+from .process_pipepline import ProcessPipeline
+from .video_predictor import SAM2VideoPredictor
\ No newline at end of file

wan/modules/animate/preprocess/human_visualization.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import cv2
+import time
+import math
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import Dict, List
+import random
+from pose2d_utils import AAPoseMeta
+
+
+def draw_handpose(canvas, keypoints, hand_score_th=0.6):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    eps = 0.01
+
+    H, W, C = canvas.shape
+    stickwidth = max(int(min(H, W) / 200), 1)
+
+    edges = [
+        [0, 1],
+        [1, 2],
+        [2, 3],
+        [3, 4],
+        [0, 5],
+        [5, 6],
+        [6, 7],
+        [7, 8],
+        [0, 9],
+        [9, 10],
+        [10, 11],
+        [11, 12],
+        [0, 13],
+        [13, 14],
+        [14, 15],
+        [15, 16],
+        [0, 17],
+        [17, 18],
+        [18, 19],
+        [19, 20],
+    ]
+
+    for ie, (e1, e2) in enumerate(edges):
+        k1 = keypoints[e1]
+        k2 = keypoints[e2]
+        if k1 is None or k2 is None:
+            continue
+        if k1[2] < hand_score_th or k2[2] < hand_score_th:
+            continue
+
+        x1 = int(k1[0])
+        y1 = int(k1[1])
+        x2 = int(k2[0])
+        y2 = int(k2[1])
+        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+            cv2.line(
+                canvas,
+                (x1, y1),
+                (x2, y2),
+                matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
+                thickness=stickwidth,
+            )
+
+    for keypoint in keypoints:
+
+        if keypoint is None:
+            continue
+        if keypoint[2] < hand_score_th:
+            continue
+
+        x, y = keypoint[0], keypoint[1]
+        x = int(x)
+        y = int(y)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), stickwidth, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_handpose_new(canvas, keypoints, stickwidth_type='v2', hand_score_th=0.6):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    eps = 0.01
+
+    H, W, C = canvas.shape
+    if stickwidth_type == 'v1':
+        stickwidth = max(int(min(H, W) / 200), 1)
+    elif stickwidth_type == 'v2':
+        stickwidth = max(max(int(min(H, W) / 200) - 1, 1) // 2, 1)
+
+    edges = [
+        [0, 1],
+        [1, 2],
+        [2, 3],
+        [3, 4],
+        [0, 5],
+        [5, 6],
+        [6, 7],
+        [7, 8],
+        [0, 9],
+        [9, 10],
+        [10, 11],
+        [11, 12],
+        [0, 13],
+        [13, 14],
+        [14, 15],
+        [15, 16],
+        [0, 17],
+        [17, 18],
+        [18, 19],
+        [19, 20],
+    ]
+
+    for ie, (e1, e2) in enumerate(edges):
+        k1 = keypoints[e1]
+        k2 = keypoints[e2]
+        if k1 is None or k2 is None:
+            continue
+        if k1[2] < hand_score_th or k2[2] < hand_score_th:
+            continue
+
+        x1 = int(k1[0])
+        y1 = int(k1[1])
+        x2 = int(k2[0])
+        y2 = int(k2[1])
+        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+            cv2.line(
+                canvas,
+                (x1, y1),
+                (x2, y2),
+                matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
+                thickness=stickwidth,
+            )
+
+    for keypoint in keypoints:
+
+        if keypoint is None:
+            continue
+        if keypoint[2] < hand_score_th:
+            continue
+
+        x, y = keypoint[0], keypoint[1]
+        x = int(x)
+        y = int(y)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), stickwidth, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_ellipse_by_2kp(img, keypoint1, keypoint2, color, threshold=0.6):
+    H, W, C = img.shape
+    stickwidth = max(int(min(H, W) / 200), 1)
+
+    if keypoint1[-1] < threshold or keypoint2[-1] < threshold:
+        return img
+
+    Y = np.array([keypoint1[0], keypoint2[0]])
+    X = np.array([keypoint1[1], keypoint2[1]])
+    mX = np.mean(X)
+    mY = np.mean(Y)
+    length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+    angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+    polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+    cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color])
+    return img
+
+
+def split_pose2d_kps_to_aa(kp2ds: np.ndarray) -> List[np.ndarray]:
+    """Convert the 133 keypoints from pose2d to body and hands keypoints.
+
+    Args:
+        kp2ds (np.ndarray): [133, 2]
+
+    Returns:
+        List[np.ndarray]: _description_
+    """
+    kp2ds_body = (
+        kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]]
+        + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]
+    ) / 2
+    kp2ds_lhand = kp2ds[91:112]
+    kp2ds_rhand = kp2ds[112:133]
+    return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy()
+
+
+def draw_aapose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200, draw_hand=True, draw_head=True):
+    kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1)
+    kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1)
+    kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1)
+    pose_img = draw_aapose(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand, stick_width_norm=stick_width_norm, draw_hand=draw_hand, draw_head=draw_head)
+    return pose_img
+
+def draw_aapose_by_meta_new(img, meta: AAPoseMeta, threshold=0.5, stickwidth_type='v2', draw_hand=True, draw_head=True):
+    kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1)
+    kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1)
+    kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1)
+    pose_img = draw_aapose_new(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand,
+                               stickwidth_type=stickwidth_type, draw_hand=draw_hand, draw_head=draw_head)
+    return pose_img
+
+def draw_hand_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200):
+    kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None] * 0], axis=1)
+    kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1)
+    kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1)
+    pose_img = draw_aapose(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand, stick_width_norm=stick_width_norm, draw_hand=True, draw_head=False)
+    return pose_img
+
+
+def draw_aaface_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200, draw_hand=False, draw_head=True):
+    kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1)
+    # kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1)
+    # kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1)
+    pose_img = draw_M(img, kp2ds, threshold, kp2ds_lhand=None, kp2ds_rhand=None, stick_width_norm=stick_width_norm, draw_hand=draw_hand, draw_head=draw_head)
+    return pose_img
+
+
+def draw_aanose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=100, draw_hand=False):
+    kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1)
+    # kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1)
+    # kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1)
+    pose_img = draw_nose(img, kp2ds, threshold, kp2ds_lhand=None, kp2ds_rhand=None, stick_width_norm=stick_width_norm, draw_hand=draw_hand)
+    return pose_img
+
+
+def gen_face_motion_seq(img, metas: List[AAPoseMeta], threshold=0.5, stick_width_norm=200):
+
+    return 
+
+
+def draw_M(
+    img,
+    kp2ds,
+    threshold=0.6,
+    data_to_json=None,
+    idx=-1,
+    kp2ds_lhand=None,
+    kp2ds_rhand=None,
+    draw_hand=False,
+    stick_width_norm=200,
+    draw_head=True
+):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+
+    new_kep_list = [
+        "Nose",
+        "Neck",
+        "RShoulder",
+        "RElbow",
+        "RWrist",  # No.4
+        "LShoulder",
+        "LElbow",
+        "LWrist",  # No.7
+        "RHip",
+        "RKnee",
+        "RAnkle",  # No.10
+        "LHip",
+        "LKnee",
+        "LAnkle",  # No.13
+        "REye",
+        "LEye",
+        "REar",
+        "LEar",
+        "LToe",
+        "RToe",
+    ]
+    # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \
+    #              kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+    kp2ds = kp2ds.copy()
+    # import ipdb; ipdb.set_trace()
+    kp2ds[[1,2,3,4,5,6,7,8,9,10,11,12,13,18,19], 2] = 0
+    if not draw_head:
+        kp2ds[[0,14,15,16,17], 2] = 0
+    kp2ds_body = kp2ds
+    # kp2ds_body = kp2ds_body[:18]
+
+    # kp2ds_lhand = kp2ds.copy()[91:112]
+    # kp2ds_rhand = kp2ds.copy()[112:133]
+
+    limbSeq = [
+        # [2, 3],
+        # [2, 6],  # shoulders
+        # [3, 4],
+        # [4, 5],  # left arm
+        # [6, 7],
+        # [7, 8],  # right arm
+        # [2, 9],
+        # [9, 10],
+        # [10, 11],  # right leg
+        # [2, 12],
+        # [12, 13],
+        # [13, 14],  # left leg
+        # [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],  # face (nose, eyes, ears)
+        # [14, 19],
+        # [11, 20],  # foot
+    ]
+
+    colors = [
+        # [255, 0, 0],
+        # [255, 85, 0],
+        # [255, 170, 0],
+        # [255, 255, 0],
+        # [170, 255, 0],
+        # [85, 255, 0],
+        # [0, 255, 0],
+        # [0, 255, 85],
+        # [0, 255, 170],
+        # [0, 255, 255],
+        # [0, 170, 255],
+        # [0, 85, 255],
+        # [0, 0, 255],
+        # [85, 0, 255],
+        [170, 0, 255],
+        [255, 0, 255],
+        [255, 0, 170],
+        [255, 0, 85],
+        # foot
+        # [200, 200, 0],
+        # [100, 100, 0],
+    ]
+
+    H, W, C = img.shape
+    stickwidth = max(int(min(H, W) / stick_width_norm), 1)
+
+    for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)):
+        keypoint1 = kp2ds_body[k1_index - 1]
+        keypoint2 = kp2ds_body[k2_index - 1]
+
+        if keypoint1[-1] < threshold or keypoint2[-1] < threshold:
+            continue
+
+        Y = np.array([keypoint1[0], keypoint2[0]])
+        X = np.array([keypoint1[1], keypoint2[1]])
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color])
+
+    for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)):
+        if keypoint[-1] < threshold:
+            continue
+        x, y = keypoint[0], keypoint[1]
+        # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+        cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1)
+
+    if draw_hand:
+        img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold)
+        img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold)
+
+    kp2ds_body[:, 0] /= W
+    kp2ds_body[:, 1] /= H
+
+    if data_to_json is not None:
+        if idx == -1:
+            data_to_json.append(
+                {
+                    "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1),
+                    "height": H,
+                    "width": W,
+                    "category_id": 1,
+                    "keypoints_body": kp2ds_body.tolist(),
+                    "keypoints_left_hand": kp2ds_lhand.tolist(),
+                    "keypoints_right_hand": kp2ds_rhand.tolist(),
+                }
+            )
+        else:
+            data_to_json[idx] = {
+                "image_id": "frame_{:05d}.jpg".format(idx + 1),
+                "height": H,
+                "width": W,
+                "category_id": 1,
+                "keypoints_body": kp2ds_body.tolist(),
+                "keypoints_left_hand": kp2ds_lhand.tolist(),
+                "keypoints_right_hand": kp2ds_rhand.tolist(),
+            }
+    return img
+
+
+def draw_nose(
+    img,
+    kp2ds,
+    threshold=0.6,
+    data_to_json=None,
+    idx=-1,
+    kp2ds_lhand=None,
+    kp2ds_rhand=None,
+    draw_hand=False,
+    stick_width_norm=200,
+):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+
+    new_kep_list = [
+        "Nose",
+        "Neck",
+        "RShoulder",
+        "RElbow",
+        "RWrist",  # No.4
+        "LShoulder",
+        "LElbow",
+        "LWrist",  # No.7
+        "RHip",
+        "RKnee",
+        "RAnkle",  # No.10
+        "LHip",
+        "LKnee",
+        "LAnkle",  # No.13
+        "REye",
+        "LEye",
+        "REar",
+        "LEar",
+        "LToe",
+        "RToe",
+    ]
+    # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \
+    #              kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+    kp2ds = kp2ds.copy()
+    kp2ds[1:, 2] = 0
+    # kp2ds[0, 2] = 1
+    kp2ds_body = kp2ds
+    # kp2ds_body = kp2ds_body[:18]
+
+    # kp2ds_lhand = kp2ds.copy()[91:112]
+    # kp2ds_rhand = kp2ds.copy()[112:133]
+
+    limbSeq = [
+        # [2, 3],
+        # [2, 6],  # shoulders
+        # [3, 4],
+        # [4, 5],  # left arm
+        # [6, 7],
+        # [7, 8],  # right arm
+        # [2, 9],
+        # [9, 10],
+        # [10, 11],  # right leg
+        # [2, 12],
+        # [12, 13],
+        # [13, 14],  # left leg
+        # [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],  # face (nose, eyes, ears)
+        # [14, 19],
+        # [11, 20],  # foot
+    ]
+
+    colors = [
+        # [255, 0, 0],
+        # [255, 85, 0],
+        # [255, 170, 0],
+        # [255, 255, 0],
+        # [170, 255, 0],
+        # [85, 255, 0],
+        # [0, 255, 0],
+        # [0, 255, 85],
+        # [0, 255, 170],
+        # [0, 255, 255],
+        # [0, 170, 255],
+        # [0, 85, 255],
+        # [0, 0, 255],
+        # [85, 0, 255],
+        [170, 0, 255],
+        # [255, 0, 255],
+        # [255, 0, 170],
+        # [255, 0, 85],
+        # foot
+        # [200, 200, 0],
+        # [100, 100, 0],
+    ]
+
+    H, W, C = img.shape
+    stickwidth = max(int(min(H, W) / stick_width_norm), 1)
+
+    # for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)):
+    #     keypoint1 = kp2ds_body[k1_index - 1]
+    #     keypoint2 = kp2ds_body[k2_index - 1]
+
+    #     if keypoint1[-1] < threshold or keypoint2[-1] < threshold:
+    #         continue
+
+    #     Y = np.array([keypoint1[0], keypoint2[0]])
+    #     X = np.array([keypoint1[1], keypoint2[1]])
+    #     mX = np.mean(X)
+    #     mY = np.mean(Y)
+    #     length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+    #     angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+    #     polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+    #     cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color])
+
+    for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)):
+        if keypoint[-1] < threshold:
+            continue
+        x, y = keypoint[0], keypoint[1]
+        # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+        cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1)
+
+    if draw_hand:
+        img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold)
+        img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold)
+
+    kp2ds_body[:, 0] /= W
+    kp2ds_body[:, 1] /= H
+
+    if data_to_json is not None:
+        if idx == -1:
+            data_to_json.append(
+                {
+                    "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1),
+                    "height": H,
+                    "width": W,
+                    "category_id": 1,
+                    "keypoints_body": kp2ds_body.tolist(),
+                    "keypoints_left_hand": kp2ds_lhand.tolist(),
+                    "keypoints_right_hand": kp2ds_rhand.tolist(),
+                }
+            )
+        else:
+            data_to_json[idx] = {
+                "image_id": "frame_{:05d}.jpg".format(idx + 1),
+                "height": H,
+                "width": W,
+                "category_id": 1,
+                "keypoints_body": kp2ds_body.tolist(),
+                "keypoints_left_hand": kp2ds_lhand.tolist(),
+                "keypoints_right_hand": kp2ds_rhand.tolist(),
+            }
+    return img
+
+
+def draw_aapose(
+    img,
+    kp2ds,
+    threshold=0.6,
+    data_to_json=None,
+    idx=-1,
+    kp2ds_lhand=None,
+    kp2ds_rhand=None,
+    draw_hand=False,
+    stick_width_norm=200,
+    draw_head=True
+):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+
+    new_kep_list = [
+        "Nose",
+        "Neck",
+        "RShoulder",
+        "RElbow",
+        "RWrist",  # No.4
+        "LShoulder",
+        "LElbow",
+        "LWrist",  # No.7
+        "RHip",
+        "RKnee",
+        "RAnkle",  # No.10
+        "LHip",
+        "LKnee",
+        "LAnkle",  # No.13
+        "REye",
+        "LEye",
+        "REar",
+        "LEar",
+        "LToe",
+        "RToe",
+    ]
+    # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \
+    #              kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+    kp2ds = kp2ds.copy()
+    if not draw_head:
+        kp2ds[[0,14,15,16,17], 2] = 0
+    kp2ds_body = kp2ds
+
+    # kp2ds_lhand = kp2ds.copy()[91:112]
+    # kp2ds_rhand = kp2ds.copy()[112:133]
+
+    limbSeq = [
+        [2, 3],
+        [2, 6],  # shoulders
+        [3, 4],
+        [4, 5],  # left arm
+        [6, 7],
+        [7, 8],  # right arm
+        [2, 9],
+        [9, 10],
+        [10, 11],  # right leg
+        [2, 12],
+        [12, 13],
+        [13, 14],  # left leg
+        [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],  # face (nose, eyes, ears)
+        [14, 19],
+        [11, 20],  # foot
+    ]
+
+    colors = [
+        [255, 0, 0],
+        [255, 85, 0],
+        [255, 170, 0],
+        [255, 255, 0],
+        [170, 255, 0],
+        [85, 255, 0],
+        [0, 255, 0],
+        [0, 255, 85],
+        [0, 255, 170],
+        [0, 255, 255],
+        [0, 170, 255],
+        [0, 85, 255],
+        [0, 0, 255],
+        [85, 0, 255],
+        [170, 0, 255],
+        [255, 0, 255],
+        [255, 0, 170],
+        [255, 0, 85],
+        # foot
+        [200, 200, 0],
+        [100, 100, 0],
+    ]
+
+    H, W, C = img.shape
+    stickwidth = max(int(min(H, W) / stick_width_norm), 1)
+
+    for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)):
+        keypoint1 = kp2ds_body[k1_index - 1]
+        keypoint2 = kp2ds_body[k2_index - 1]
+
+        if keypoint1[-1] < threshold or keypoint2[-1] < threshold:
+            continue
+
+        Y = np.array([keypoint1[0], keypoint2[0]])
+        X = np.array([keypoint1[1], keypoint2[1]])
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color])
+
+    for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)):
+        if keypoint[-1] < threshold:
+            continue
+        x, y = keypoint[0], keypoint[1]
+        # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+        cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1)
+
+    if draw_hand:
+        img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold)
+        img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold)
+
+    kp2ds_body[:, 0] /= W
+    kp2ds_body[:, 1] /= H
+
+    if data_to_json is not None:
+        if idx == -1:
+            data_to_json.append(
+                {
+                    "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1),
+                    "height": H,
+                    "width": W,
+                    "category_id": 1,
+                    "keypoints_body": kp2ds_body.tolist(),
+                    "keypoints_left_hand": kp2ds_lhand.tolist(),
+                    "keypoints_right_hand": kp2ds_rhand.tolist(),
+                }
+            )
+        else:
+            data_to_json[idx] = {
+                "image_id": "frame_{:05d}.jpg".format(idx + 1),
+                "height": H,
+                "width": W,
+                "category_id": 1,
+                "keypoints_body": kp2ds_body.tolist(),
+                "keypoints_left_hand": kp2ds_lhand.tolist(),
+                "keypoints_right_hand": kp2ds_rhand.tolist(),
+            }
+    return img
+
+
+def draw_aapose_new(
+    img,
+    kp2ds,
+    threshold=0.6,
+    data_to_json=None,
+    idx=-1,
+    kp2ds_lhand=None,
+    kp2ds_rhand=None,
+    draw_hand=False,
+    stickwidth_type='v2',
+    draw_head=True
+):
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+
+    new_kep_list = [
+        "Nose",
+        "Neck",
+        "RShoulder",
+        "RElbow",
+        "RWrist",  # No.4
+        "LShoulder",
+        "LElbow",
+        "LWrist",  # No.7
+        "RHip",
+        "RKnee",
+        "RAnkle",  # No.10
+        "LHip",
+        "LKnee",
+        "LAnkle",  # No.13
+        "REye",
+        "LEye",
+        "REar",
+        "LEar",
+        "LToe",
+        "RToe",
+    ]
+    # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \
+    #              kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+    kp2ds = kp2ds.copy()
+    if not draw_head:
+        kp2ds[[0,14,15,16,17], 2] = 0
+    kp2ds_body = kp2ds
+
+    # kp2ds_lhand = kp2ds.copy()[91:112]
+    # kp2ds_rhand = kp2ds.copy()[112:133]
+
+    limbSeq = [
+        [2, 3],
+        [2, 6],  # shoulders
+        [3, 4],
+        [4, 5],  # left arm
+        [6, 7],
+        [7, 8],  # right arm
+        [2, 9],
+        [9, 10],
+        [10, 11],  # right leg
+        [2, 12],
+        [12, 13],
+        [13, 14],  # left leg
+        [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],  # face (nose, eyes, ears)
+        [14, 19],
+        [11, 20],  # foot
+    ]
+
+    colors = [
+        [255, 0, 0],
+        [255, 85, 0],
+        [255, 170, 0],
+        [255, 255, 0],
+        [170, 255, 0],
+        [85, 255, 0],
+        [0, 255, 0],
+        [0, 255, 85],
+        [0, 255, 170],
+        [0, 255, 255],
+        [0, 170, 255],
+        [0, 85, 255],
+        [0, 0, 255],
+        [85, 0, 255],
+        [170, 0, 255],
+        [255, 0, 255],
+        [255, 0, 170],
+        [255, 0, 85],
+        # foot
+        [200, 200, 0],
+        [100, 100, 0],
+    ]
+
+    H, W, C = img.shape
+    H, W, C = img.shape
+
+    if stickwidth_type == 'v1':
+        stickwidth = max(int(min(H, W) / 200), 1)
+    elif stickwidth_type == 'v2':
+        stickwidth = max(int(min(H, W) / 200) - 1, 1)
+    else:
+        raise
+
+    for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)):
+        keypoint1 = kp2ds_body[k1_index - 1]
+        keypoint2 = kp2ds_body[k2_index - 1]
+
+        if keypoint1[-1] < threshold or keypoint2[-1] < threshold:
+            continue
+
+        Y = np.array([keypoint1[0], keypoint2[0]])
+        X = np.array([keypoint1[1], keypoint2[1]])
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color])
+
+    for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)):
+        if keypoint[-1] < threshold:
+            continue
+        x, y = keypoint[0], keypoint[1]
+        # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+        cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1)
+
+    if draw_hand:
+        img = draw_handpose_new(img, kp2ds_lhand, stickwidth_type=stickwidth_type, hand_score_th=threshold)
+        img = draw_handpose_new(img, kp2ds_rhand, stickwidth_type=stickwidth_type, hand_score_th=threshold)
+
+    kp2ds_body[:, 0] /= W
+    kp2ds_body[:, 1] /= H
+
+    if data_to_json is not None:
+        if idx == -1:
+            data_to_json.append(
+                {
+                    "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1),
+                    "height": H,
+                    "width": W,
+                    "category_id": 1,
+                    "keypoints_body": kp2ds_body.tolist(),
+                    "keypoints_left_hand": kp2ds_lhand.tolist(),
+                    "keypoints_right_hand": kp2ds_rhand.tolist(),
+                }
+            )
+        else:
+            data_to_json[idx] = {
+                "image_id": "frame_{:05d}.jpg".format(idx + 1),
+                "height": H,
+                "width": W,
+                "category_id": 1,
+                "keypoints_body": kp2ds_body.tolist(),
+                "keypoints_left_hand": kp2ds_lhand.tolist(),
+                "keypoints_right_hand": kp2ds_rhand.tolist(),
+            }
+    return img
+
+
+def draw_bbox(img, bbox, color=(255, 0, 0)):
+    img = load_image(img)
+    bbox = [int(bbox_tmp) for bbox_tmp in bbox]
+    cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
+    return img
+
+
+def draw_kp2ds(img, kp2ds, threshold=0, color=(255, 0, 0), skeleton=None, reverse=False):
+    img = load_image(img, reverse)
+
+    if skeleton is not None:
+        if skeleton == "coco17":
+            skeleton_list = [
+                [6, 8],
+                [8, 10],
+                [5, 7],
+                [7, 9],
+                [11, 13],
+                [13, 15],
+                [12, 14],
+                [14, 16],
+                [5, 6],
+                [6, 12],
+                [12, 11],
+                [11, 5],
+            ]
+            color_list = [
+                (255, 0, 0),
+                (0, 255, 0),
+                (0, 0, 255),
+                (255, 255, 0),
+                (255, 0, 255),
+                (0, 255, 255),
+            ]
+        elif skeleton == "cocowholebody":
+            skeleton_list = [
+                [6, 8],
+                [8, 10],
+                [5, 7],
+                [7, 9],
+                [11, 13],
+                [13, 15],
+                [12, 14],
+                [14, 16],
+                [5, 6],
+                [6, 12],
+                [12, 11],
+                [11, 5],
+                [15, 17],
+                [15, 18],
+                [15, 19],
+                [16, 20],
+                [16, 21],
+                [16, 22],
+                [91, 92, 93, 94, 95],
+                [91, 96, 97, 98, 99],
+                [91, 100, 101, 102, 103],
+                [91, 104, 105, 106, 107],
+                [91, 108, 109, 110, 111],
+                [112, 113, 114, 115, 116],
+                [112, 117, 118, 119, 120],
+                [112, 121, 122, 123, 124],
+                [112, 125, 126, 127, 128],
+                [112, 129, 130, 131, 132],
+            ]
+            color_list = [
+                (255, 0, 0),
+                (0, 255, 0),
+                (0, 0, 255),
+                (255, 255, 0),
+                (255, 0, 255),
+                (0, 255, 255),
+            ]
+        else:
+            color_list = [color]
+        for _idx, _skeleton in enumerate(skeleton_list):
+            for i in range(len(_skeleton) - 1):
+                cv2.line(
+                    img,
+                    (int(kp2ds[_skeleton[i], 0]), int(kp2ds[_skeleton[i], 1])),
+                    (int(kp2ds[_skeleton[i + 1], 0]), int(kp2ds[_skeleton[i + 1], 1])),
+                    color_list[_idx % len(color_list)],
+                    3,
+                )
+
+    for _idx, kp2d in enumerate(kp2ds):
+        if kp2d[2] > threshold:
+            cv2.circle(img, (int(kp2d[0]), int(kp2d[1])), 3, color, -1)
+            # cv2.putText(img,
+            #         str(_idx),
+            #         (int(kp2d[0, i, 0])*1,
+            #             int(kp2d[0, i, 1])*1),
+            #         cv2.FONT_HERSHEY_SIMPLEX,
+            #         0.75,
+            #         color,
+            #         2
+            #         )
+
+    return img
+
+
+def draw_mask(img, mask, background=0, return_rgba=False):
+    img = load_image(img)
+    h, w, _ = img.shape
+    if type(background) == int:
+        background = np.ones((h, w, 3)).astype(np.uint8) * 255 * background
+    backgournd = cv2.resize(background, (w, h))
+    img_rgba = np.concatenate([img, mask], -1)
+    return alphaMerge(img_rgba, background, 0, 0, return_rgba=True)
+
+
+def draw_pcd(pcd_list, save_path=None):
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection="3d")
+
+    color_list = ["r", "g", "b", "y", "p"]
+
+    for _idx, _pcd in enumerate(pcd_list):
+        ax.scatter(_pcd[:, 0], _pcd[:, 1], _pcd[:, 2], c=color_list[_idx], marker="o")
+
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+
+    if save_path is not None:
+        plt.savefig(save_path)
+    else:
+        plt.savefig("tmp.png")
+
+
+def load_image(img, reverse=False):
+    if type(img) == str:
+        img = cv2.imread(img)
+    if reverse:
+        img = img.astype(np.float32)
+        img = img[:, :, ::-1]
+        img = img.astype(np.uint8)
+    return img
+
+
+def draw_skeleten(meta):
+    kps = []
+    for i, kp in enumerate(meta["keypoints_body"]):
+        if kp is None:
+            # if kp is None:
+            kps.append([0, 0, 0])
+        else:
+            kps.append([*kp, 1])
+    kps = np.array(kps)
+
+    kps[:, 0] *= meta["width"]
+    kps[:, 1] *= meta["height"]
+    pose_img = np.zeros([meta["height"], meta["width"], 3], dtype=np.uint8)
+
+    pose_img = draw_aapose(
+        pose_img,
+        kps,
+        draw_hand=True,
+        kp2ds_lhand=meta["keypoints_left_hand"],
+        kp2ds_rhand=meta["keypoints_right_hand"],
+    )
+    return pose_img
+
+
+def draw_skeleten_with_pncc(pncc: np.ndarray, meta: Dict) -> np.ndarray:
+    """
+    Args:
+        pncc: [H,W,3]
+        meta: required keys: keypoints_body: [N, 3] keypoints_left_hand, keypoints_right_hand
+    Return:
+        np.ndarray [H, W, 3]
+    """
+    # preprocess keypoints
+    kps = []
+    for i, kp in enumerate(meta["keypoints_body"]):
+        if kp is None:
+            # if kp is None:
+            kps.append([0, 0, 0])
+        elif i in [14, 15, 16, 17]:
+            kps.append([0, 0, 0])
+        else:
+            kps.append([*kp])
+    kps = np.stack(kps)
+
+    kps[:, 0] *= pncc.shape[1]
+    kps[:, 1] *= pncc.shape[0]
+
+    # draw neck
+    canvas = np.zeros_like(pncc)
+    if kps[0][2] > 0.6 and kps[1][2] > 0.6:
+        canvas = draw_ellipse_by_2kp(canvas, kps[0], kps[1], [0, 0, 255])
+
+    # draw pncc
+    mask = (pncc > 0).max(axis=2)
+    canvas[mask] = pncc[mask]
+    pncc = canvas
+
+    # draw other skeleten
+    kps[0] = 0
+
+    meta["keypoints_left_hand"][:, 0] *= meta["width"]
+    meta["keypoints_left_hand"][:, 1] *= meta["height"]
+
+    meta["keypoints_right_hand"][:, 0] *= meta["width"]
+    meta["keypoints_right_hand"][:, 1] *= meta["height"]
+    pose_img = draw_aapose(
+        pncc,
+        kps,
+        draw_hand=True,
+        kp2ds_lhand=meta["keypoints_left_hand"],
+        kp2ds_rhand=meta["keypoints_right_hand"],
+    )
+    return pose_img
+
+
+FACE_CUSTOM_STYLE = {
+    "eyeball": {"indexs": [68, 69], "color": [255, 255, 255], "connect": False},
+    "left_eyebrow": {"indexs": [17, 18, 19, 20, 21], "color": [0, 255, 0]},
+    "right_eyebrow": {"indexs": [22, 23, 24, 25, 26], "color": [0, 0, 255]},
+    "left_eye": {"indexs": [36, 37, 38, 39, 40, 41], "color": [255, 255, 0], "close": True},
+    "right_eye": {"indexs": [42, 43, 44, 45, 46, 47], "color": [255, 0, 255], "close": True},
+    "mouth_outside": {"indexs": list(range(48, 60)), "color": [100, 255, 50], "close": True},
+    "mouth_inside": {"indexs": [60, 61, 62, 63, 64, 65, 66, 67], "color": [255, 100, 50], "close": True},
+}
+
+
+def draw_face_kp(img, kps, thickness=2, style=FACE_CUSTOM_STYLE):
+    """
+    Args:
+        img: [H, W, 3]
+        kps: [70, 2]
+    """
+    img = img.copy()
+    for key, item in style.items():
+        pts = np.array(kps[item["indexs"]]).astype(np.int32)
+        connect = item.get("connect", True)
+        color = item["color"]
+        close = item.get("close", False)
+        if connect:
+            cv2.polylines(img, [pts], close, color, thickness=thickness)
+        else:
+            for kp in pts:
+                kp = np.array(kp).astype(np.int32)
+                cv2.circle(img, kp, thickness * 2, color=color, thickness=-1)
+    return img
+
+
+def draw_traj(metas: List[AAPoseMeta], threshold=0.6):
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+                [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+                [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], [100, 255, 50], [255, 100, 50], 
+                # foot
+                [200, 200, 0],
+                [100, 100, 0]
+                ]
+    limbSeq = [
+                    [1, 2], [1, 5],     # shoulders
+                    [2, 3], [3, 4],     # left arm
+                    [5, 6], [6, 7],     # right arm
+                    [1, 8], [8, 9], [9, 10],    # right leg 
+                    [1, 11], [11, 12], [12, 13],  # left leg
+                     # face (nose, eyes, ears)
+                    [13, 18], [10, 19] # foot
+                ]
+    
+    face_seq = [[1, 0], [0, 14], [14, 16], [0, 15], [15, 17]] 
+    kp_body = np.array([meta.kps_body for meta in metas])
+    kp_body_p = np.array([meta.kps_body_p for meta in metas])
+    
+    
+    face_seq = random.sample(face_seq, 2)
+
+    kp_lh = np.array([meta.kps_lhand for meta in metas])
+    kp_rh = np.array([meta.kps_rhand for meta in metas])
+
+    kp_lh_p = np.array([meta.kps_lhand_p for meta in metas])
+    kp_rh_p = np.array([meta.kps_rhand_p for meta in metas])
+
+    # kp_lh = np.concatenate([kp_lh, kp_lh_p], axis=-1)
+    # kp_rh = np.concatenate([kp_rh, kp_rh_p], axis=-1)
+    
+    new_limbSeq = []
+    key_point_list = []
+    for _idx, ((k1_index, k2_index)) in enumerate(limbSeq):
+        
+        vis = (kp_body_p[:, k1_index] > threshold) * (kp_body_p[:, k2_index] > threshold) * 1
+        if vis.sum() * 1.0 / vis.shape[0] > 0.4:
+            new_limbSeq.append([k1_index, k2_index])
+
+    for _idx, ((k1_index, k2_index)) in enumerate(limbSeq):
+
+        keypoint1 = kp_body[:, k1_index - 1]
+        keypoint2 = kp_body[:, k2_index - 1]
+        interleave = random.randint(4, 7)
+        randind = random.randint(0, interleave - 1)
+        # randind = random.rand(range(interleave), sampling_num)
+
+        Y = np.array([keypoint1[:, 0], keypoint2[:, 0]]) 
+        X = np.array([keypoint1[:, 1], keypoint2[:, 1]])
+
+        vis = (keypoint1[:, -1] > threshold) * (keypoint2[:, -1] > threshold) * 1
+
+        # for randidx in randind:
+        t = randind / interleave
+        x = (1-t)*Y[0, :] + t*Y[1, :]
+        y = (1-t)*X[0, :] + t*X[1, :]
+
+        # np.array([1])
+        x = x.astype(int)
+        y = y.astype(int)
+
+        new_array = np.array([x, y, vis]).T
+        
+        key_point_list.append(new_array)
+    
+    indx_lh = random.randint(0, kp_lh.shape[1] - 1)
+    lh = kp_lh[:, indx_lh, :]
+    lh_p = kp_lh_p[:, indx_lh:indx_lh+1]
+    lh = np.concatenate([lh, lh_p], axis=-1)
+    
+    indx_rh = random.randint(0, kp_rh.shape[1] - 1)
+    rh = kp_rh[:, random.randint(0, kp_rh.shape[1] - 1), :]
+    rh_p = kp_rh_p[:, indx_rh:indx_rh+1]
+    rh = np.concatenate([rh, rh_p], axis=-1)
+
+
+
+    lh[-1, :] = (lh[-1, :] > threshold) * 1
+    rh[-1, :] = (rh[-1, :] > threshold) * 1
+
+    # print(rh.shape, new_array.shape)
+    # exit()
+    key_point_list.append(lh.astype(int))
+    key_point_list.append(rh.astype(int))
+
+    
+    key_points_list = np.stack(key_point_list)
+    num_points = len(key_points_list)
+    sample_colors = random.sample(colors, num_points)
+
+    stickwidth = max(int(min(metas[0].width, metas[0].height) / 150), 2)
+
+    image_list_ori = []
+    for i in range(key_points_list.shape[-2]):
+        _image_vis = np.zeros((metas[0].width, metas[0].height, 3))
+        points = key_points_list[:, i, :]
+        for idx, point in enumerate(points):
+            x, y, vis = point
+            if vis == 1:
+                cv2.circle(_image_vis, (x, y), stickwidth, sample_colors[idx], thickness=-1)
+        
+        image_list_ori.append(_image_vis)
+    
+    return image_list_ori
+
+    return [np.zeros([meta.width, meta.height, 3], dtype=np.uint8) for meta in metas]
+
+
+if __name__ == "__main__":
+    meta = {
+        "image_id": "00472.jpg",
+        "height": 540,
+        "width": 414,
+        "category_id": 1,
+        "keypoints_body": [
+            [0.5084776947463768, 0.11350188078703703],
+            [0.504467655495169, 0.20419560185185184],
+            [0.3982016153381642, 0.198046875],
+            [0.3841664779589372, 0.34869068287037036],
+            [0.3901815368357488, 0.4670536747685185],
+            [0.610733695652174, 0.2103443287037037],
+            [0.6167487545289855, 0.3517650462962963],
+            [0.6448190292874396, 0.4762767650462963],
+            [0.4523371452294686, 0.47320240162037036],
+            [0.4503321256038647, 0.6776475694444445],
+            [0.47639738073671495, 0.8544234664351852],
+            [0.5766483620169082, 0.47320240162037036],
+            [0.5666232638888888, 0.6761103877314815],
+            [0.534542949879227, 0.863646556712963],
+            [0.4864224788647343, 0.09505570023148148],
+            [0.5285278910024155, 0.09351851851851851],
+            [0.46236224335748793, 0.10581597222222222],
+            [0.5586031853864735, 0.10274160879629629],
+            [0.4994551064311594, 0.9405056423611111],
+            [0.4152442821557971, 0.9312825520833333],
+        ],
+        "keypoints_left_hand": [
+            [267.78515625, 263.830078125, 1.2840936183929443],
+            [265.294921875, 269.640625, 1.2546794414520264],
+            [263.634765625, 277.111328125, 1.2863062620162964],
+            [262.8046875, 285.412109375, 1.267038345336914],
+            [261.14453125, 292.8828125, 1.280144453048706],
+            [273.595703125, 281.26171875, 1.2592815160751343],
+            [271.10546875, 291.22265625, 1.3256099224090576],
+            [265.294921875, 294.54296875, 1.2368024587631226],
+            [261.14453125, 294.54296875, 0.9771889448165894],
+            [274.42578125, 282.091796875, 1.250044584274292],
+            [269.4453125, 291.22265625, 1.2571144104003906],
+            [264.46484375, 292.8828125, 1.177802324295044],
+            [260.314453125, 292.052734375, 0.9283463358879089],
+            [273.595703125, 282.091796875, 1.1834490299224854],
+            [269.4453125, 290.392578125, 1.188171625137329],
+            [265.294921875, 290.392578125, 1.192609429359436],
+            [261.974609375, 289.5625, 0.9366656541824341],
+            [271.935546875, 281.26171875, 1.0946396589279175],
+            [268.615234375, 287.072265625, 0.9906131029129028],
+            [265.294921875, 287.90234375, 1.0219476222991943],
+            [262.8046875, 287.072265625, 0.9240120053291321],
+        ],
+        "keypoints_right_hand": [
+            [161.53515625, 258.849609375, 1.2069408893585205],
+            [168.17578125, 263.0, 1.1846840381622314],
+            [173.986328125, 269.640625, 1.1435924768447876],
+            [173.986328125, 277.94140625, 1.1802611351013184],
+            [173.986328125, 286.2421875, 1.2599592208862305],
+            [165.685546875, 275.451171875, 1.0633569955825806],
+            [167.345703125, 286.2421875, 1.1693341732025146],
+            [169.8359375, 291.22265625, 1.2698509693145752],
+            [170.666015625, 294.54296875, 1.0619274377822876],
+            [160.705078125, 276.28125, 1.0995020866394043],
+            [163.1953125, 287.90234375, 1.2735884189605713],
+            [166.515625, 291.22265625, 1.339503526687622],
+            [169.005859375, 294.54296875, 1.0835273265838623],
+            [157.384765625, 277.111328125, 1.0866981744766235],
+            [161.53515625, 287.072265625, 1.2468621730804443],
+            [164.025390625, 289.5625, 1.2817761898040771],
+            [166.515625, 292.052734375, 1.099466323852539],
+            [155.724609375, 277.111328125, 1.1065717935562134],
+            [159.044921875, 285.412109375, 1.1924479007720947],
+            [160.705078125, 287.072265625, 1.1304771900177002],
+            [162.365234375, 287.90234375, 1.0040509700775146],
+        ],
+    }
+    demo_meta = AAPoseMeta(meta)
+    res = draw_traj([demo_meta]*5)
+    cv2.imwrite("traj.png", res[0][..., ::-1])

wan/modules/animate/preprocess/pose2d.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import cv2
+from typing import Union, List
+
+import numpy as np
+import torch
+import onnxruntime
+
+from pose2d_utils import (
+    read_img,
+    box_convert_simple,
+    bbox_from_detector,
+    crop,
+    keypoints_from_heatmaps,
+    load_pose_metas_from_kp2ds_seq
+)
+
+
+class SimpleOnnxInference(object):
+    def __init__(self, checkpoint, device='cuda', reverse_input=False, **kwargs):
+        if isinstance(device, str):
+            device = torch.device(device)
+        if device.type == 'cuda':
+            device = '{}:{}'.format(device.type, device.index)
+            providers = [("CUDAExecutionProvider", {"device_id": device[-1:] if device[-1] in [str(_i) for _i in range(10)] else "0"}), "CPUExecutionProvider"]
+        else:
+            providers = ["CPUExecutionProvider"]
+        self.device = device
+        if not os.path.exists(checkpoint):
+            raise RuntimeError("{} is not existed!".format(checkpoint))
+        
+        if os.path.isdir(checkpoint):
+            checkpoint = os.path.join(checkpoint, 'end2end.onnx')
+
+        self.session = onnxruntime.InferenceSession(checkpoint,
+                                                    providers=providers
+                                                    )
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_name = self.session.get_outputs()[0].name
+        self.input_resolution = self.session.get_inputs()[0].shape[2:] if not reverse_input else self.session.get_inputs()[0].shape[2:][::-1]
+        self.input_resolution = np.array(self.input_resolution)
+        
+
+    def __call__(self, *args, **kwargs):
+        return self.forward(*args, **kwargs)
+    
+
+    def get_output_names(self):
+        output_names = []
+        for node in self.session.get_outputs():
+            output_names.append(node.name)
+        return output_names
+
+
+    def set_device(self, device):
+        if isinstance(device, str):
+            device = torch.device(device)
+        if device.type == 'cuda':
+            device = '{}:{}'.format(device.type, device.index)
+            providers = [("CUDAExecutionProvider", {"device_id": device[-1:] if device[-1] in [str(_i) for _i in range(10)] else "0"}), "CPUExecutionProvider"]
+        else:
+            providers = ["CPUExecutionProvider"]
+        self.session.set_providers(providers)
+        self.device = device
+
+
+class Yolo(SimpleOnnxInference):
+    def __init__(self, checkpoint, device='cuda', threshold_conf=0.05, threshold_multi_persons=0.1, input_resolution=(640, 640), threshold_iou=0.5, threshold_bbox_shape_ratio=0.4, cat_id=[1], select_type='max', strict=True, sorted_func=None, **kwargs):
+        super(Yolo, self).__init__(checkpoint, device=device, **kwargs)
+        
+        model_inputs = self.session.get_inputs()
+        input_shape = model_inputs[0].shape
+
+        self.input_width = 640
+        self.input_height = 640
+        
+        self.threshold_multi_persons = threshold_multi_persons
+        self.threshold_conf = threshold_conf
+        self.threshold_iou = threshold_iou
+        self.threshold_bbox_shape_ratio = threshold_bbox_shape_ratio
+        self.input_resolution = input_resolution
+        self.cat_id = cat_id
+        self.select_type = select_type
+        self.strict = strict
+        self.sorted_func = sorted_func
+        
+        
+    def preprocess(self, input_image):
+        """
+        Preprocesses the input image before performing inference.
+
+        Returns:
+            image_data: Preprocessed image data ready for inference.
+        """
+        img = read_img(input_image)
+        # Get the height and width of the input image
+        img_height, img_width = img.shape[:2]
+        # Resize the image to match the input shape
+        img = cv2.resize(img, (self.input_resolution[1], self.input_resolution[0]))
+        # Normalize the image data by dividing it by 255.0
+        image_data = np.array(img) / 255.0
+        # Transpose the image to have the channel dimension as the first dimension
+        image_data = np.transpose(image_data, (2, 0, 1))  # Channel first
+        # Expand the dimensions of the image data to match the expected input shape
+        # image_data = np.expand_dims(image_data, axis=0).astype(np.float32)
+        image_data = image_data.astype(np.float32)
+        # Return the preprocessed image data
+        return image_data, np.array([img_height, img_width])
+
+    
+    def postprocess(self, output, shape_raw, cat_id=[1]):
+        """
+        Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs.
+
+        Args:
+            input_image (numpy.ndarray): The input image.
+            output (numpy.ndarray): The output of the model.
+
+        Returns:
+            numpy.ndarray: The input image with detections drawn on it.
+        """
+        # Transpose and squeeze the output to match the expected shape
+
+        outputs = np.squeeze(output)
+        if len(outputs.shape) == 1:
+            outputs = outputs[None]
+        if output.shape[-1] != 6 and output.shape[1] == 84:
+            outputs = np.transpose(outputs)
+        
+        # Get the number of rows in the outputs array
+        rows = outputs.shape[0]
+
+        # Calculate the scaling factors for the bounding box coordinates
+        x_factor = shape_raw[1] / self.input_width
+        y_factor = shape_raw[0] / self.input_height
+
+        # Lists to store the bounding boxes, scores, and class IDs of the detections
+        boxes = []
+        scores = []
+        class_ids = []
+
+        if outputs.shape[-1] == 6:
+            max_scores = outputs[:, 4]
+            classid = outputs[:, -1]
+            
+            threshold_conf_masks = max_scores >= self.threshold_conf
+            classid_masks = classid[threshold_conf_masks] != 3.14159
+
+            max_scores = max_scores[threshold_conf_masks][classid_masks]
+            classid = classid[threshold_conf_masks][classid_masks]
+
+            boxes = outputs[:, :4][threshold_conf_masks][classid_masks]
+            boxes[:, [0, 2]] *= x_factor
+            boxes[:, [1, 3]] *= y_factor
+            boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
+            boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
+            boxes = boxes.astype(np.int32)
+
+        else:
+            classes_scores = outputs[:, 4:]
+            max_scores = np.amax(classes_scores, -1)
+            threshold_conf_masks = max_scores >= self.threshold_conf
+
+            classid = np.argmax(classes_scores[threshold_conf_masks], -1)
+
+            classid_masks = classid!=3.14159
+            
+            classes_scores = classes_scores[threshold_conf_masks][classid_masks]
+            max_scores = max_scores[threshold_conf_masks][classid_masks]
+            classid = classid[classid_masks]
+    
+            xywh = outputs[:, :4][threshold_conf_masks][classid_masks]
+
+            x = xywh[:, 0:1]
+            y = xywh[:, 1:2]
+            w = xywh[:, 2:3]
+            h = xywh[:, 3:4]
+    
+            left = ((x - w / 2) * x_factor)
+            top = ((y - h / 2) * y_factor)
+            width = (w * x_factor)
+            height = (h * y_factor)
+            boxes = np.concatenate([left, top, width, height], axis=-1).astype(np.int32)
+
+        boxes = boxes.tolist()
+        scores = max_scores.tolist()
+        class_ids = classid.tolist()
+
+        # Apply non-maximum suppression to filter out overlapping bounding boxes
+        indices = cv2.dnn.NMSBoxes(boxes, scores, self.threshold_conf, self.threshold_iou)
+        # Iterate over the selected indices after non-maximum suppression
+        
+        results = []
+        for i in indices:
+            # Get the box, score, and class ID corresponding to the index
+            box = box_convert_simple(boxes[i], 'xywh2xyxy')
+            score = scores[i]
+            class_id = class_ids[i]
+            results.append(box + [score] + [class_id])
+            # # Draw the detection on the input image
+
+        # Return the modified input image
+        return np.array(results)
+
+    
+    def process_results(self, results, shape_raw, cat_id=[1], single_person=True):
+        if isinstance(results, tuple):
+            det_results = results[0]
+        else:
+            det_results = results
+
+        person_results = []
+        person_count = 0
+        if len(results):
+            max_idx = -1
+            max_bbox_size = shape_raw[0] * shape_raw[1] * -10
+            max_bbox_shape = -1
+            
+            bboxes = []
+            idx_list = []
+            for i in range(results.shape[0]):
+                bbox = results[i]
+                if (bbox[-1] + 1 in cat_id) and (bbox[-2] > self.threshold_conf):
+                    idx_list.append(i)
+                    bbox_shape = max((bbox[2] - bbox[0]), ((bbox[3] - bbox[1])))
+                    if bbox_shape > max_bbox_shape:
+                        max_bbox_shape = bbox_shape
+            
+            results = results[idx_list]
+
+            for i in range(results.shape[0]):
+                bbox = results[i]
+                bboxes.append(bbox)
+                if self.select_type == 'max':
+                    bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1]))
+                elif self.select_type == 'center':
+                    bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1
+                bbox_shape = max((bbox[2] - bbox[0]), ((bbox[3] - bbox[1])))
+                if bbox_size > max_bbox_size:
+                    if (self.strict or max_idx != -1) and bbox_shape < max_bbox_shape * self.threshold_bbox_shape_ratio:
+                        continue
+                    max_bbox_size = bbox_size
+                    max_bbox_shape = bbox_shape
+                    max_idx = i
+
+            if self.sorted_func is not None and len(bboxes) > 0:
+                max_idx = self.sorted_func(bboxes, shape_raw)
+                bbox = bboxes[max_idx]
+                if self.select_type == 'max':
+                    max_bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1]))
+                elif self.select_type == 'center':
+                    max_bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1
+                
+            if max_idx != -1:
+                person_count = 1
+
+            if max_idx != -1:
+                person = {}
+                person['bbox'] = results[max_idx, :5]
+                person['track_id'] = int(0)
+                person_results.append(person)
+
+            for i in range(results.shape[0]):
+                bbox = results[i]
+                if (bbox[-1] + 1 in cat_id) and (bbox[-2] > self.threshold_conf):
+                    if self.select_type == 'max':
+                        bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1]))
+                    elif self.select_type == 'center':
+                        bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1
+                    if i != max_idx and bbox_size > max_bbox_size * self.threshold_multi_persons and bbox_size < max_bbox_size:
+                        person_count += 1
+                        if not single_person:
+                            person = {}
+                            person['bbox'] = results[i, :5]
+                            person['track_id'] = int(person_count - 1)
+                            person_results.append(person)                   
+            return person_results
+        else:
+            return None
+        
+
+    def postprocess_threading(self, outputs, shape_raw, person_results, i, single_person=True, **kwargs):
+        result = self.postprocess(outputs[i], shape_raw[i], cat_id=self.cat_id)
+        result = self.process_results(result, shape_raw[i], cat_id=self.cat_id, single_person=single_person)
+        if result is not None and len(result) != 0:
+            person_results[i] = result
+
+
+    def forward(self, img, shape_raw, **kwargs):
+        """
+        Performs inference using an ONNX model and returns the output image with drawn detections.
+
+        Returns:
+            output_img: The output image with drawn detections.
+        """
+        if isinstance(img, torch.Tensor):
+            img = img.cpu().numpy()
+            shape_raw = shape_raw.cpu().numpy()
+
+        outputs = self.session.run(None, {self.session.get_inputs()[0].name: img})[0]
+        person_results = [[{'bbox': np.array([0., 0., 1.*shape_raw[i][1], 1.*shape_raw[i][0], -1]), 'track_id': -1}] for i in range(len(outputs))]
+
+        for i in range(len(outputs)):
+            self.postprocess_threading(outputs, shape_raw, person_results, i, **kwargs)         
+        return person_results
+
+
+class ViTPose(SimpleOnnxInference):
+    def __init__(self, checkpoint, device='cuda', **kwargs):
+        super(ViTPose, self).__init__(checkpoint, device=device)
+
+    def forward(self, img, center, scale, **kwargs):
+        heatmaps = self.session.run([], {self.session.get_inputs()[0].name: img})[0]
+        points, prob = keypoints_from_heatmaps(heatmaps=heatmaps,
+                                            center=center,
+                                            scale=scale*200,
+                                            unbiased=True, 
+                                            use_udp=False)
+        return np.concatenate([points, prob], axis=2)
+
+
+    @staticmethod
+    def preprocess(img, bbox=None, input_resolution=(256, 192), rescale=1.25, mask=None, **kwargs):
+        if bbox is None or bbox[-1] <= 0 or (bbox[2] - bbox[0]) < 10 or (bbox[3] - bbox[1]) < 10:
+            bbox = np.array([0, 0, img.shape[1], img.shape[0]])
+        
+        bbox_xywh = bbox
+        if mask is not None:
+            img = np.where(mask>128, img, mask)
+
+        if isinstance(input_resolution, int):
+            center, scale = bbox_from_detector(bbox_xywh, (input_resolution, input_resolution), rescale=rescale)
+            img, new_shape, old_xy, new_xy = crop(img, center, scale, (input_resolution, input_resolution))
+        else:
+            center, scale = bbox_from_detector(bbox_xywh, input_resolution, rescale=rescale)
+            img, new_shape, old_xy, new_xy = crop(img, center, scale, (input_resolution[0], input_resolution[1]))
+
+        IMG_NORM_MEAN = np.array([0.485, 0.456, 0.406])
+        IMG_NORM_STD = np.array([0.229, 0.224, 0.225])
+        img_norm = (img / 255. - IMG_NORM_MEAN) / IMG_NORM_STD
+        img_norm = img_norm.transpose(2, 0, 1).astype(np.float32)
+        return img_norm, np.array(center), np.array(scale)
+
+
+class Pose2d:
+    def __init__(self, checkpoint, detector_checkpoint=None, device='cuda', **kwargs):
+
+        if detector_checkpoint is not None:
+            self.detector = Yolo(detector_checkpoint, device)
+        else:
+            self.detector = None
+
+        self.model = ViTPose(checkpoint, device)
+        self.device = device
+
+    def load_images(self, inputs):
+        """
+        Load images from various input types.
+        
+        Args:
+            inputs (Union[str, np.ndarray, List[np.ndarray]]): Input can be file path, 
+                     single image array, or list of image arrays
+            
+        Returns:
+            List[np.ndarray]: List of RGB image arrays
+            
+        Raises:
+            ValueError: If file format is unsupported or image cannot be read
+        """
+        if isinstance(inputs, str):
+            if inputs.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
+                cap = cv2.VideoCapture(inputs)
+                frames = []
+                while True:
+                    ret, frame = cap.read()
+                    if not ret:
+                        break
+                    frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+                cap.release()
+                images = frames
+            elif inputs.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
+                img = cv2.cvtColor(cv2.imread(inputs), cv2.COLOR_BGR2RGB)
+                if img is None:
+                    raise ValueError(f"Cannot read image: {inputs}")
+                images = [img]
+            else:
+                raise ValueError(f"Unsupported file format: {inputs}")
+                
+        elif isinstance(inputs, np.ndarray):
+            images = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in inputs]
+        elif isinstance(inputs, list):
+            images = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in inputs]
+        return images
+
+    def __call__(
+        self, 
+        inputs: Union[str, np.ndarray, List[np.ndarray]],
+        return_image: bool = False,
+        **kwargs
+    ):
+        """
+        Process input and estimate 2D keypoints.
+        
+        Args:
+            inputs (Union[str, np.ndarray, List[np.ndarray]]): Input can be file path,
+                     single image array, or list of image arrays
+            **kwargs: Additional arguments for processing
+            
+        Returns:
+            np.ndarray: Array of detected 2D keypoints for all input images
+        """
+        images = self.load_images(inputs)
+        H, W = images[0].shape[:2]
+        if self.detector is not None:
+            bboxes = []
+            for _image in images:
+                img, shape = self.detector.preprocess(_image)
+                bboxes.append(self.detector(img[None], shape[None])[0][0]["bbox"])
+        else:
+            bboxes = [None] * len(images)
+
+        kp2ds = []
+        for _image, _bbox in zip(images, bboxes):
+            img, center, scale = self.model.preprocess(_image, _bbox)
+            kp2ds.append(self.model(img[None], center[None], scale[None]))
+        kp2ds = np.concatenate(kp2ds, 0)
+        metas = load_pose_metas_from_kp2ds_seq(kp2ds, width=W, height=H)
+        return metas
\ No newline at end of file

wan/modules/animate/preprocess/pose2d_utils.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import warnings
+import cv2
+import numpy as np
+from typing import List
+from PIL import Image
+
+
+def box_convert_simple(box, convert_type='xyxy2xywh'):
+    if convert_type == 'xyxy2xywh':
+        return [box[0], box[1], box[2] - box[0], box[3] - box[1]]
+    elif convert_type == 'xywh2xyxy':
+        return [box[0], box[1], box[2] + box[0], box[3] + box[1]]
+    elif convert_type == 'xyxy2ctwh':
+        return [(box[0] + box[2]) / 2, (box[1] + box[3]) / 2, box[2] - box[0], box[3] - box[1]]
+    elif convert_type == 'ctwh2xyxy':
+        return [box[0] - box[2] // 2, box[1] - box[3] // 2, box[0] + (box[2] - box[2] // 2), box[1] + (box[3] - box[3] // 2)]
+
+def read_img(image, convert='RGB', check_exist=False):
+    if isinstance(image, str):
+        if check_exist and not osp.exists(image):
+            return None
+        try:
+            img = Image.open(image)
+            if convert:
+                img = img.convert(convert)
+        except:
+            raise IOError('File error: ', image)
+        return np.asarray(img)
+    else:
+        if isinstance(image, np.ndarray):
+            if convert:
+                return image[..., ::-1]
+        else:
+            if convert:
+                img = img.convert(convert)
+            return np.asarray(img)
+
+class AAPoseMeta:
+    def __init__(self, meta=None, kp2ds=None):
+        self.image_id = ""
+        self.height = 0
+        self.width = 0
+
+        self.kps_body: np.ndarray = None
+        self.kps_lhand: np.ndarray = None
+        self.kps_rhand: np.ndarray = None
+        self.kps_face: np.ndarray = None
+        self.kps_body_p: np.ndarray = None
+        self.kps_lhand_p: np.ndarray = None
+        self.kps_rhand_p: np.ndarray = None
+        self.kps_face_p: np.ndarray = None
+
+
+        if meta is not None:
+            self.load_from_meta(meta)
+        elif kp2ds is not None:
+            self.load_from_kp2ds(kp2ds)
+    
+    def is_valid(self, kp, p, threshold):
+        x, y = kp
+        if x < 0 or y < 0 or x > self.width or y > self.height or p < threshold: 
+            return False
+        else:
+            return True
+    
+    def get_bbox(self, kp, kp_p, threshold=0.5):
+        kps = kp[kp_p > threshold]
+        if kps.size == 0:
+            return 0, 0, 0, 0
+        x0, y0 = kps.min(axis=0)
+        x1, y1 = kps.max(axis=0)
+        return x0, y0, x1, y1
+    
+    def crop(self, x0, y0, x1, y1):
+        all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face]
+        for kps in all_kps:
+            if kps is not None:
+                kps[:, 0] -= x0
+                kps[:, 1] -= y0
+        self.width = x1 - x0
+        self.height = y1 - y0
+        return self
+    
+    def resize(self, width, height):
+        scale_x = width / self.width
+        scale_y = height / self.height
+        all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face]
+        for kps in all_kps:
+            if kps is not None:
+                kps[:, 0] *= scale_x
+                kps[:, 1] *= scale_y
+        self.width = width
+        self.height = height
+        return self
+
+    
+    def get_kps_body_with_p(self, normalize=False):
+        kps_body = self.kps_body.copy()
+        if normalize:
+            kps_body = kps_body / np.array([self.width, self.height])
+
+        return np.concatenate([kps_body, self.kps_body_p[:, None]])
+    
+    @staticmethod
+    def from_kps_face(kps_face: np.ndarray, height: int, width: int):
+
+        pose_meta = AAPoseMeta()
+        pose_meta.kps_face = kps_face[:, :2]
+        if kps_face.shape[1] == 3:
+            pose_meta.kps_face_p = kps_face[:, 2]
+        else:
+            pose_meta.kps_face_p = kps_face[:, 0] * 0 + 1
+        pose_meta.height = height
+        pose_meta.width = width
+        return pose_meta
+
+    @staticmethod
+    def from_kps_body(kps_body: np.ndarray, height: int, width: int):
+
+        pose_meta = AAPoseMeta()
+        pose_meta.kps_body = kps_body[:, :2]
+        pose_meta.kps_body_p = kps_body[:, 2]
+        pose_meta.height = height
+        pose_meta.width = width
+        return pose_meta
+    @staticmethod
+    def from_humanapi_meta(meta):
+        pose_meta = AAPoseMeta()
+        width, height = meta["width"], meta["height"]
+        pose_meta.width = width
+        pose_meta.height = height
+        pose_meta.kps_body = meta["keypoints_body"][:, :2] * (width, height)
+        pose_meta.kps_body_p = meta["keypoints_body"][:, 2]
+        pose_meta.kps_lhand = meta["keypoints_left_hand"][:, :2] * (width, height)
+        pose_meta.kps_lhand_p = meta["keypoints_left_hand"][:, 2]
+        pose_meta.kps_rhand = meta["keypoints_right_hand"][:, :2] * (width, height)
+        pose_meta.kps_rhand_p = meta["keypoints_right_hand"][:, 2]
+        if 'keypoints_face' in meta:
+            pose_meta.kps_face = meta["keypoints_face"][:, :2] * (width, height)
+            pose_meta.kps_face_p = meta["keypoints_face"][:, 2]
+        return pose_meta
+    
+    def load_from_meta(self, meta, norm_body=True, norm_hand=False):
+        
+        self.image_id = meta.get("image_id", "00000.png")
+        self.height = meta["height"]
+        self.width = meta["width"]
+        kps_body_p = []
+        kps_body = []
+        for kp in meta["keypoints_body"]:
+            if kp is None:
+                kps_body.append([0, 0])
+                kps_body_p.append(0)
+            else:
+                kps_body.append(kp)
+                kps_body_p.append(1)
+
+        self.kps_body = np.array(kps_body)
+        self.kps_body[:, 0] *= self.width
+        self.kps_body[:, 1] *= self.height
+        self.kps_body_p = np.array(kps_body_p)
+
+        self.kps_lhand = np.array(meta["keypoints_left_hand"])[:, :2]
+        self.kps_lhand_p = np.array(meta["keypoints_left_hand"])[:, 2]
+        self.kps_rhand = np.array(meta["keypoints_right_hand"])[:, :2]
+        self.kps_rhand_p = np.array(meta["keypoints_right_hand"])[:, 2]
+
+    @staticmethod
+    def load_from_kp2ds(kp2ds: List[np.ndarray], width: int, height: int): 
+        """input 133x3 numpy keypoints and output AAPoseMeta
+
+        Args:
+            kp2ds (List[np.ndarray]): _description_
+            width (int): _description_
+            height (int): _description_
+
+        Returns:
+            _type_: _description_
+        """
+        pose_meta = AAPoseMeta()
+        pose_meta.width = width
+        pose_meta.height = height
+        kps_body = (kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+        kps_lhand = kp2ds[91:112]
+        kps_rhand = kp2ds[112:133]
+        kps_face = np.concatenate([kp2ds[23:23+68], kp2ds[1:3]], axis=0)
+        pose_meta.kps_body = kps_body[:, :2]
+        pose_meta.kps_body_p = kps_body[:, 2]
+        pose_meta.kps_lhand = kps_lhand[:, :2]
+        pose_meta.kps_lhand_p = kps_lhand[:, 2]
+        pose_meta.kps_rhand = kps_rhand[:, :2]
+        pose_meta.kps_rhand_p = kps_rhand[:, 2]
+        pose_meta.kps_face = kps_face[:, :2]
+        pose_meta.kps_face_p = kps_face[:, 2]
+        return pose_meta
+    
+    @staticmethod
+    def from_dwpose(dwpose_det_res, height, width):
+        pose_meta = AAPoseMeta()
+        pose_meta.kps_body = dwpose_det_res["bodies"]["candidate"]
+        pose_meta.kps_body_p = dwpose_det_res["bodies"]["score"]
+        pose_meta.kps_body[:, 0] *= width
+        pose_meta.kps_body[:, 1] *= height
+
+        pose_meta.kps_lhand, pose_meta.kps_rhand = dwpose_det_res["hands"]
+        pose_meta.kps_lhand[:, 0] *= width
+        pose_meta.kps_lhand[:, 1] *= height
+        pose_meta.kps_rhand[:, 0] *= width
+        pose_meta.kps_rhand[:, 1] *= height
+        pose_meta.kps_lhand_p, pose_meta.kps_rhand_p = dwpose_det_res["hands_score"]
+
+        pose_meta.kps_face = dwpose_det_res["faces"][0]
+        pose_meta.kps_face[:, 0] *= width
+        pose_meta.kps_face[:, 1] *= height
+        pose_meta.kps_face_p = dwpose_det_res["faces_score"][0]
+        return pose_meta
+
+    def save_json(self):
+        pass
+
+    def draw_aapose(self, img, threshold=0.5, stick_width_norm=200, draw_hand=True, draw_head=True):
+        from .human_visualization import draw_aapose_by_meta
+        return draw_aapose_by_meta(img, self, threshold, stick_width_norm, draw_hand, draw_head)
+
+    
+    def translate(self, x0, y0):
+        all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face]
+        for kps in all_kps:
+            if kps is not None:
+                kps[:, 0] -= x0
+                kps[:, 1] -= y0
+
+    def scale(self, sx, sy):
+        all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face]
+        for kps in all_kps:
+            if kps is not None:
+                kps[:, 0] *= sx
+                kps[:, 1] *= sy
+    
+    def padding_resize2(self, height=512, width=512):
+        """kps will be changed inplace
+
+        """
+
+        all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face]
+
+        ori_height, ori_width = self.height, self.width
+
+        if (ori_height / ori_width) > (height / width):
+            new_width = int(height / ori_height * ori_width)
+            padding = int((width - new_width) / 2)
+            padding_width = padding
+            padding_height = 0
+            scale = height / ori_height 
+
+            for kps in all_kps:
+                if kps is not None:
+                    kps[:, 0] = kps[:, 0] * scale + padding
+                    kps[:, 1] = kps[:, 1] * scale
+
+        else:
+            new_height = int(width / ori_width * ori_height)
+            padding = int((height - new_height) / 2)
+            padding_width = 0
+            padding_height = padding 
+            scale = width / ori_width
+            for kps in all_kps:
+                if kps is not None:
+                    kps[:, 1] = kps[:, 1] * scale + padding
+                    kps[:, 0] = kps[:, 0] * scale
+
+
+        self.width = width
+        self.height = height
+        return self
+        
+
+def transform_preds(coords, center, scale, output_size, use_udp=False):
+    """Get final keypoint predictions from heatmaps and apply scaling and
+    translation to map them back to the image.
+
+    Note:
+        num_keypoints: K
+
+    Args:
+        coords (np.ndarray[K, ndims]):
+
+            * If ndims=2, corrds are predicted keypoint location.
+            * If ndims=4, corrds are composed of (x, y, scores, tags)
+            * If ndims=5, corrds are composed of (x, y, scores, tags,
+              flipped_tags)
+
+        center (np.ndarray[2, ]): Center of the bounding box (x, y).
+        scale (np.ndarray[2, ]): Scale of the bounding box
+            wrt [width, height].
+        output_size (np.ndarray[2, ] | list(2,)): Size of the
+            destination heatmaps.
+        use_udp (bool): Use unbiased data processing
+
+    Returns:
+        np.ndarray: Predicted coordinates in the images.
+    """
+    assert coords.shape[1] in (2, 4, 5)
+    assert len(center) == 2
+    assert len(scale) == 2
+    assert len(output_size) == 2
+
+    # Recover the scale which is normalized by a factor of 200.
+    # scale = scale * 200.0
+
+    if use_udp:
+        scale_x = scale[0] / (output_size[0] - 1.0)
+        scale_y = scale[1] / (output_size[1] - 1.0)
+    else:
+        scale_x = scale[0] / output_size[0]
+        scale_y = scale[1] / output_size[1]
+
+    target_coords = np.ones_like(coords)
+    target_coords[:, 0] = coords[:, 0] * scale_x + center[0] - scale[0] * 0.5
+    target_coords[:, 1] = coords[:, 1] * scale_y + center[1] - scale[1] * 0.5
+
+    return target_coords
+
+
+def _calc_distances(preds, targets, mask, normalize):
+    """Calculate the normalized distances between preds and target.
+
+    Note:
+        batch_size: N
+        num_keypoints: K
+        dimension of keypoints: D (normally, D=2 or D=3)
+
+    Args:
+        preds (np.ndarray[N, K, D]): Predicted keypoint location.
+        targets (np.ndarray[N, K, D]): Groundtruth keypoint location.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+        normalize (np.ndarray[N, D]): Typical value is heatmap_size
+
+    Returns:
+        np.ndarray[K, N]: The normalized distances. \
+            If target keypoints are missing, the distance is -1.
+    """
+    N, K, _ = preds.shape
+    # set mask=0 when normalize==0
+    _mask = mask.copy()
+    _mask[np.where((normalize == 0).sum(1))[0], :] = False
+    distances = np.full((N, K), -1, dtype=np.float32)
+    # handle invalid values
+    normalize[np.where(normalize <= 0)] = 1e6
+    distances[_mask] = np.linalg.norm(
+        ((preds - targets) / normalize[:, None, :])[_mask], axis=-1)
+    return distances.T
+
+
+def _distance_acc(distances, thr=0.5):
+    """Return the percentage below the distance threshold, while ignoring
+    distances values with -1.
+
+    Note:
+        batch_size: N
+    Args:
+        distances (np.ndarray[N, ]): The normalized distances.
+        thr (float): Threshold of the distances.
+
+    Returns:
+        float: Percentage of distances below the threshold. \
+            If all target keypoints are missing, return -1.
+    """
+    distance_valid = distances != -1
+    num_distance_valid = distance_valid.sum()
+    if num_distance_valid > 0:
+        return (distances[distance_valid] < thr).sum() / num_distance_valid
+    return -1
+
+
+def _get_max_preds(heatmaps):
+    """Get keypoint predictions from score maps.
+
+    Note:
+        batch_size: N
+        num_keypoints: K
+        heatmap height: H
+        heatmap width: W
+
+    Args:
+        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
+
+    Returns:
+        tuple: A tuple containing aggregated results.
+
+        - preds (np.ndarray[N, K, 2]): Predicted keypoint location.
+        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+    """
+    assert isinstance(heatmaps,
+                      np.ndarray), ('heatmaps should be numpy.ndarray')
+    assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
+
+    N, K, _, W = heatmaps.shape
+    heatmaps_reshaped = heatmaps.reshape((N, K, -1))
+    idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1))
+    maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1))
+
+    preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
+    preds[:, :, 0] = preds[:, :, 0] % W
+    preds[:, :, 1] = preds[:, :, 1] // W
+
+    preds = np.where(np.tile(maxvals, (1, 1, 2)) > 0.0, preds, -1)
+    return preds, maxvals
+
+
+def _get_max_preds_3d(heatmaps):
+    """Get keypoint predictions from 3D score maps.
+
+    Note:
+        batch size: N
+        num keypoints: K
+        heatmap depth size: D
+        heatmap height: H
+        heatmap width: W
+
+    Args:
+        heatmaps (np.ndarray[N, K, D, H, W]): model predicted heatmaps.
+
+    Returns:
+        tuple: A tuple containing aggregated results.
+
+        - preds (np.ndarray[N, K, 3]): Predicted keypoint location.
+        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+    """
+    assert isinstance(heatmaps, np.ndarray), \
+        ('heatmaps should be numpy.ndarray')
+    assert heatmaps.ndim == 5, 'heatmaps should be 5-ndim'
+
+    N, K, D, H, W = heatmaps.shape
+    heatmaps_reshaped = heatmaps.reshape((N, K, -1))
+    idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1))
+    maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1))
+
+    preds = np.zeros((N, K, 3), dtype=np.float32)
+    _idx = idx[..., 0]
+    preds[..., 2] = _idx // (H * W)
+    preds[..., 1] = (_idx // W) % H
+    preds[..., 0] = _idx % W
+
+    preds = np.where(maxvals > 0.0, preds, -1)
+    return preds, maxvals
+
+
+def pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None):
+    """Calculate the pose accuracy of PCK for each individual keypoint and the
+    averaged accuracy across all keypoints from heatmaps.
+
+    Note:
+        PCK metric measures accuracy of the localization of the body joints.
+        The distances between predicted positions and the ground-truth ones
+        are typically normalized by the bounding box size.
+        The threshold (thr) of the normalized distance is commonly set
+        as 0.05, 0.1 or 0.2 etc.
+
+        - batch_size: N
+        - num_keypoints: K
+        - heatmap height: H
+        - heatmap width: W
+
+    Args:
+        output (np.ndarray[N, K, H, W]): Model output heatmaps.
+        target (np.ndarray[N, K, H, W]): Groundtruth heatmaps.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+        thr (float): Threshold of PCK calculation. Default 0.05.
+        normalize (np.ndarray[N, 2]): Normalization factor for H&W.
+
+    Returns:
+        tuple: A tuple containing keypoint accuracy.
+
+        - np.ndarray[K]: Accuracy of each keypoint.
+        - float: Averaged accuracy across all keypoints.
+        - int: Number of valid keypoints.
+    """
+    N, K, H, W = output.shape
+    if K == 0:
+        return None, 0, 0
+    if normalize is None:
+        normalize = np.tile(np.array([[H, W]]), (N, 1))
+
+    pred, _ = _get_max_preds(output)
+    gt, _ = _get_max_preds(target)
+    return keypoint_pck_accuracy(pred, gt, mask, thr, normalize)
+
+
+def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):
+    """Calculate the pose accuracy of PCK for each individual keypoint and the
+    averaged accuracy across all keypoints for coordinates.
+
+    Note:
+        PCK metric measures accuracy of the localization of the body joints.
+        The distances between predicted positions and the ground-truth ones
+        are typically normalized by the bounding box size.
+        The threshold (thr) of the normalized distance is commonly set
+        as 0.05, 0.1 or 0.2 etc.
+
+        - batch_size: N
+        - num_keypoints: K
+
+    Args:
+        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
+        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+        thr (float): Threshold of PCK calculation.
+        normalize (np.ndarray[N, 2]): Normalization factor for H&W.
+
+    Returns:
+        tuple: A tuple containing keypoint accuracy.
+
+        - acc (np.ndarray[K]): Accuracy of each keypoint.
+        - avg_acc (float): Averaged accuracy across all keypoints.
+        - cnt (int): Number of valid keypoints.
+    """
+    distances = _calc_distances(pred, gt, mask, normalize)
+
+    acc = np.array([_distance_acc(d, thr) for d in distances])
+    valid_acc = acc[acc >= 0]
+    cnt = len(valid_acc)
+    avg_acc = valid_acc.mean() if cnt > 0 else 0
+    return acc, avg_acc, cnt
+
+
+def keypoint_auc(pred, gt, mask, normalize, num_step=20):
+    """Calculate the pose accuracy of PCK for each individual keypoint and the
+    averaged accuracy across all keypoints for coordinates.
+
+    Note:
+        - batch_size: N
+        - num_keypoints: K
+
+    Args:
+        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
+        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+        normalize (float): Normalization factor.
+
+    Returns:
+        float: Area under curve.
+    """
+    nor = np.tile(np.array([[normalize, normalize]]), (pred.shape[0], 1))
+    x = [1.0 * i / num_step for i in range(num_step)]
+    y = []
+    for thr in x:
+        _, avg_acc, _ = keypoint_pck_accuracy(pred, gt, mask, thr, nor)
+        y.append(avg_acc)
+
+    auc = 0
+    for i in range(num_step):
+        auc += 1.0 / num_step * y[i]
+    return auc
+
+
+def keypoint_nme(pred, gt, mask, normalize_factor):
+    """Calculate the normalized mean error (NME).
+
+    Note:
+        - batch_size: N
+        - num_keypoints: K
+
+    Args:
+        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
+        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+        normalize_factor (np.ndarray[N, 2]): Normalization factor.
+
+    Returns:
+        float: normalized mean error
+    """
+    distances = _calc_distances(pred, gt, mask, normalize_factor)
+    distance_valid = distances[distances != -1]
+    return distance_valid.sum() / max(1, len(distance_valid))
+
+
+def keypoint_epe(pred, gt, mask):
+    """Calculate the end-point error.
+
+    Note:
+        - batch_size: N
+        - num_keypoints: K
+
+    Args:
+        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
+        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
+        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
+            joints, and True for visible. Invisible joints will be ignored for
+            accuracy calculation.
+
+    Returns:
+        float: Average end-point error.
+    """
+
+    distances = _calc_distances(
+        pred, gt, mask,
+        np.ones((pred.shape[0], pred.shape[2]), dtype=np.float32))
+    distance_valid = distances[distances != -1]
+    return distance_valid.sum() / max(1, len(distance_valid))
+
+
+def _taylor(heatmap, coord):
+    """Distribution aware coordinate decoding method.
+
+    Note:
+        - heatmap height: H
+        - heatmap width: W
+
+    Args:
+        heatmap (np.ndarray[H, W]): Heatmap of a particular joint type.
+        coord (np.ndarray[2,]): Coordinates of the predicted keypoints.
+
+    Returns:
+        np.ndarray[2,]: Updated coordinates.
+    """
+    H, W = heatmap.shape[:2]
+    px, py = int(coord[0]), int(coord[1])
+    if 1 < px < W - 2 and 1 < py < H - 2:
+        dx = 0.5 * (heatmap[py][px + 1] - heatmap[py][px - 1])
+        dy = 0.5 * (heatmap[py + 1][px] - heatmap[py - 1][px])
+        dxx = 0.25 * (
+            heatmap[py][px + 2] - 2 * heatmap[py][px] + heatmap[py][px - 2])
+        dxy = 0.25 * (
+            heatmap[py + 1][px + 1] - heatmap[py - 1][px + 1] -
+            heatmap[py + 1][px - 1] + heatmap[py - 1][px - 1])
+        dyy = 0.25 * (
+            heatmap[py + 2 * 1][px] - 2 * heatmap[py][px] +
+            heatmap[py - 2 * 1][px])
+        derivative = np.array([[dx], [dy]])
+        hessian = np.array([[dxx, dxy], [dxy, dyy]])
+        if dxx * dyy - dxy**2 != 0:
+            hessianinv = np.linalg.inv(hessian)
+            offset = -hessianinv @ derivative
+            offset = np.squeeze(np.array(offset.T), axis=0)
+            coord += offset
+    return coord
+
+
+def post_dark_udp(coords, batch_heatmaps, kernel=3):
+    """DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The
+    Devil is in the Details: Delving into Unbiased Data Processing for Human
+    Pose Estimation (CVPR 2020). Zhang et al. Distribution-Aware Coordinate
+    Representation for Human Pose Estimation (CVPR 2020).
+
+    Note:
+        - batch size: B
+        - num keypoints: K
+        - num persons: N
+        - height of heatmaps: H
+        - width of heatmaps: W
+
+        B=1 for bottom_up paradigm where all persons share the same heatmap.
+        B=N for top_down paradigm where each person has its own heatmaps.
+
+    Args:
+        coords (np.ndarray[N, K, 2]): Initial coordinates of human pose.
+        batch_heatmaps (np.ndarray[B, K, H, W]): batch_heatmaps
+        kernel (int): Gaussian kernel size (K) for modulation.
+
+    Returns:
+        np.ndarray([N, K, 2]): Refined coordinates.
+    """
+    if not isinstance(batch_heatmaps, np.ndarray):
+        batch_heatmaps = batch_heatmaps.cpu().numpy()
+    B, K, H, W = batch_heatmaps.shape
+    N = coords.shape[0]
+    assert (B == 1 or B == N)
+    for heatmaps in batch_heatmaps:
+        for heatmap in heatmaps:
+            cv2.GaussianBlur(heatmap, (kernel, kernel), 0, heatmap)
+    np.clip(batch_heatmaps, 0.001, 50, batch_heatmaps)
+    np.log(batch_heatmaps, batch_heatmaps)
+
+    batch_heatmaps_pad = np.pad(
+        batch_heatmaps, ((0, 0), (0, 0), (1, 1), (1, 1)),
+        mode='edge').flatten()
+
+    index = coords[..., 0] + 1 + (coords[..., 1] + 1) * (W + 2)
+    index += (W + 2) * (H + 2) * np.arange(0, B * K).reshape(-1, K)
+    index = index.astype(int).reshape(-1, 1)
+    i_ = batch_heatmaps_pad[index]
+    ix1 = batch_heatmaps_pad[index + 1]
+    iy1 = batch_heatmaps_pad[index + W + 2]
+    ix1y1 = batch_heatmaps_pad[index + W + 3]
+    ix1_y1_ = batch_heatmaps_pad[index - W - 3]
+    ix1_ = batch_heatmaps_pad[index - 1]
+    iy1_ = batch_heatmaps_pad[index - 2 - W]
+
+    dx = 0.5 * (ix1 - ix1_)
+    dy = 0.5 * (iy1 - iy1_)
+    derivative = np.concatenate([dx, dy], axis=1)
+    derivative = derivative.reshape(N, K, 2, 1)
+    dxx = ix1 - 2 * i_ + ix1_
+    dyy = iy1 - 2 * i_ + iy1_
+    dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_)
+    hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1)
+    hessian = hessian.reshape(N, K, 2, 2)
+    hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2))
+    coords -= np.einsum('ijmn,ijnk->ijmk', hessian, derivative).squeeze()
+    return coords
+
+
+def _gaussian_blur(heatmaps, kernel=11):
+    """Modulate heatmap distribution with Gaussian.
+     sigma = 0.3*((kernel_size-1)*0.5-1)+0.8
+     sigma~=3 if k=17
+     sigma=2 if k=11;
+     sigma~=1.5 if k=7;
+     sigma~=1 if k=3;
+
+    Note:
+        - batch_size: N
+        - num_keypoints: K
+        - heatmap height: H
+        - heatmap width: W
+
+    Args:
+        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
+        kernel (int): Gaussian kernel size (K) for modulation, which should
+            match the heatmap gaussian sigma when training.
+            K=17 for sigma=3 and k=11 for sigma=2.
+
+    Returns:
+        np.ndarray ([N, K, H, W]): Modulated heatmap distribution.
+    """
+    assert kernel % 2 == 1
+
+    border = (kernel - 1) // 2
+    batch_size = heatmaps.shape[0]
+    num_joints = heatmaps.shape[1]
+    height = heatmaps.shape[2]
+    width = heatmaps.shape[3]
+    for i in range(batch_size):
+        for j in range(num_joints):
+            origin_max = np.max(heatmaps[i, j])
+            dr = np.zeros((height + 2 * border, width + 2 * border),
+                          dtype=np.float32)
+            dr[border:-border, border:-border] = heatmaps[i, j].copy()
+            dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
+            heatmaps[i, j] = dr[border:-border, border:-border].copy()
+            heatmaps[i, j] *= origin_max / np.max(heatmaps[i, j])
+    return heatmaps
+
+
+def keypoints_from_regression(regression_preds, center, scale, img_size):
+    """Get final keypoint predictions from regression vectors and transform
+    them back to the image.
+
+    Note:
+        - batch_size: N
+        - num_keypoints: K
+
+    Args:
+        regression_preds (np.ndarray[N, K, 2]): model prediction.
+        center (np.ndarray[N, 2]): Center of the bounding box (x, y).
+        scale (np.ndarray[N, 2]): Scale of the bounding box
+            wrt height/width.
+        img_size (list(img_width, img_height)): model input image size.
+
+    Returns:
+        tuple:
+
+        - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
+        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+    """
+    N, K, _ = regression_preds.shape
+    preds, maxvals = regression_preds, np.ones((N, K, 1), dtype=np.float32)
+
+    preds = preds * img_size
+
+    # Transform back to the image
+    for i in range(N):
+        preds[i] = transform_preds(preds[i], center[i], scale[i], img_size)
+
+    return preds, maxvals
+
+
+def keypoints_from_heatmaps(heatmaps,
+                            center,
+                            scale,
+                            unbiased=False,
+                            post_process='default',
+                            kernel=11,
+                            valid_radius_factor=0.0546875,
+                            use_udp=False,
+                            target_type='GaussianHeatmap'):
+    """Get final keypoint predictions from heatmaps and transform them back to
+    the image.
+
+    Note:
+        - batch size: N
+        - num keypoints: K
+        - heatmap height: H
+        - heatmap width: W
+
+    Args:
+        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
+        center (np.ndarray[N, 2]): Center of the bounding box (x, y).
+        scale (np.ndarray[N, 2]): Scale of the bounding box
+            wrt height/width.
+        post_process (str/None): Choice of methods to post-process
+            heatmaps. Currently supported: None, 'default', 'unbiased',
+            'megvii'.
+        unbiased (bool): Option to use unbiased decoding. Mutually
+            exclusive with megvii.
+            Note: this arg is deprecated and unbiased=True can be replaced
+            by post_process='unbiased'
+            Paper ref: Zhang et al. Distribution-Aware Coordinate
+            Representation for Human Pose Estimation (CVPR 2020).
+        kernel (int): Gaussian kernel size (K) for modulation, which should
+            match the heatmap gaussian sigma when training.
+            K=17 for sigma=3 and k=11 for sigma=2.
+        valid_radius_factor (float): The radius factor of the positive area
+            in classification heatmap for UDP.
+        use_udp (bool): Use unbiased data processing.
+        target_type (str): 'GaussianHeatmap' or 'CombinedTarget'.
+            GaussianHeatmap: Classification target with gaussian distribution.
+            CombinedTarget: The combination of classification target
+            (response map) and regression target (offset map).
+            Paper ref: Huang et al. The Devil is in the Details: Delving into
+            Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
+
+    Returns:
+        tuple: A tuple containing keypoint predictions and scores.
+
+        - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
+        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+    """
+    # Avoid being affected
+    heatmaps = heatmaps.copy()
+
+    # detect conflicts
+    if unbiased:
+        assert post_process not in [False, None, 'megvii']
+    if post_process in ['megvii', 'unbiased']:
+        assert kernel > 0
+    if use_udp:
+        assert not post_process == 'megvii'
+
+    # normalize configs
+    if post_process is False:
+        warnings.warn(
+            'post_process=False is deprecated, '
+            'please use post_process=None instead', DeprecationWarning)
+        post_process = None
+    elif post_process is True:
+        if unbiased is True:
+            warnings.warn(
+                'post_process=True, unbiased=True is deprecated,'
+                " please use post_process='unbiased' instead",
+                DeprecationWarning)
+            post_process = 'unbiased'
+        else:
+            warnings.warn(
+                'post_process=True, unbiased=False is deprecated, '
+                "please use post_process='default' instead",
+                DeprecationWarning)
+            post_process = 'default'
+    elif post_process == 'default':
+        if unbiased is True:
+            warnings.warn(
+                'unbiased=True is deprecated, please use '
+                "post_process='unbiased' instead", DeprecationWarning)
+            post_process = 'unbiased'
+
+    # start processing
+    if post_process == 'megvii':
+        heatmaps = _gaussian_blur(heatmaps, kernel=kernel)
+
+    N, K, H, W = heatmaps.shape
+    if use_udp:
+        if target_type.lower() == 'GaussianHeatMap'.lower():
+            preds, maxvals = _get_max_preds(heatmaps)
+            preds = post_dark_udp(preds, heatmaps, kernel=kernel)
+        elif target_type.lower() == 'CombinedTarget'.lower():
+            for person_heatmaps in heatmaps:
+                for i, heatmap in enumerate(person_heatmaps):
+                    kt = 2 * kernel + 1 if i % 3 == 0 else kernel
+                    cv2.GaussianBlur(heatmap, (kt, kt), 0, heatmap)
+            # valid radius is in direct proportion to the height of heatmap.
+            valid_radius = valid_radius_factor * H
+            offset_x = heatmaps[:, 1::3, :].flatten() * valid_radius
+            offset_y = heatmaps[:, 2::3, :].flatten() * valid_radius
+            heatmaps = heatmaps[:, ::3, :]
+            preds, maxvals = _get_max_preds(heatmaps)
+            index = preds[..., 0] + preds[..., 1] * W
+            index += W * H * np.arange(0, N * K / 3)
+            index = index.astype(int).reshape(N, K // 3, 1)
+            preds += np.concatenate((offset_x[index], offset_y[index]), axis=2)
+        else:
+            raise ValueError('target_type should be either '
+                             "'GaussianHeatmap' or 'CombinedTarget'")
+    else:
+        preds, maxvals = _get_max_preds(heatmaps)
+        if post_process == 'unbiased':  # alleviate biased coordinate
+            # apply Gaussian distribution modulation.
+            heatmaps = np.log(
+                np.maximum(_gaussian_blur(heatmaps, kernel), 1e-10))
+            for n in range(N):
+                for k in range(K):
+                    preds[n][k] = _taylor(heatmaps[n][k], preds[n][k])
+        elif post_process is not None:
+            # add +/-0.25 shift to the predicted locations for higher acc.
+            for n in range(N):
+                for k in range(K):
+                    heatmap = heatmaps[n][k]
+                    px = int(preds[n][k][0])
+                    py = int(preds[n][k][1])
+                    if 1 < px < W - 1 and 1 < py < H - 1:
+                        diff = np.array([
+                            heatmap[py][px + 1] - heatmap[py][px - 1],
+                            heatmap[py + 1][px] - heatmap[py - 1][px]
+                        ])
+                        preds[n][k] += np.sign(diff) * .25
+                        if post_process == 'megvii':
+                            preds[n][k] += 0.5
+
+    # Transform back to the image
+    for i in range(N):
+        preds[i] = transform_preds(
+            preds[i], center[i], scale[i], [W, H], use_udp=use_udp)
+
+    if post_process == 'megvii':
+        maxvals = maxvals / 255.0 + 0.5
+
+    return preds, maxvals
+
+
+def keypoints_from_heatmaps3d(heatmaps, center, scale):
+    """Get final keypoint predictions from 3d heatmaps and transform them back
+    to the image.
+
+    Note:
+        - batch size: N
+        - num keypoints: K
+        - heatmap depth size: D
+        - heatmap height: H
+        - heatmap width: W
+
+    Args:
+        heatmaps (np.ndarray[N, K, D, H, W]): model predicted heatmaps.
+        center (np.ndarray[N, 2]): Center of the bounding box (x, y).
+        scale (np.ndarray[N, 2]): Scale of the bounding box
+            wrt height/width.
+
+    Returns:
+        tuple: A tuple containing keypoint predictions and scores.
+
+        - preds (np.ndarray[N, K, 3]): Predicted 3d keypoint location \
+            in images.
+        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+    """
+    N, K, D, H, W = heatmaps.shape
+    preds, maxvals = _get_max_preds_3d(heatmaps)
+    # Transform back to the image
+    for i in range(N):
+        preds[i, :, :2] = transform_preds(preds[i, :, :2], center[i], scale[i],
+                                          [W, H])
+    return preds, maxvals
+
+
+def multilabel_classification_accuracy(pred, gt, mask, thr=0.5):
+    """Get multi-label classification accuracy.
+
+    Note:
+        - batch size: N
+        - label number: L
+
+    Args:
+        pred (np.ndarray[N, L, 2]): model predicted labels.
+        gt (np.ndarray[N, L, 2]): ground-truth labels.
+        mask (np.ndarray[N, 1] or np.ndarray[N, L] ): reliability of
+        ground-truth labels.
+
+    Returns:
+        float: multi-label classification accuracy.
+    """
+    # we only compute accuracy on the samples with ground-truth of all labels.
+    valid = (mask > 0).min(axis=1) if mask.ndim == 2 else (mask > 0)
+    pred, gt = pred[valid], gt[valid]
+
+    if pred.shape[0] == 0:
+        acc = 0.0  # when no sample is with gt labels, set acc to 0.
+    else:
+        # The classification of a sample is regarded as correct
+        # only if it's correct for all labels.
+        acc = (((pred - thr) * (gt - thr)) > 0).all(axis=1).mean()
+    return acc
+
+
+
+def get_transform(center, scale, res, rot=0):
+    """Generate transformation matrix."""
+    # res: (height, width), (rows, cols)
+    crop_aspect_ratio = res[0] / float(res[1])
+    h = 200 * scale
+    w = h / crop_aspect_ratio
+    t = np.zeros((3, 3))
+    t[0, 0] = float(res[1]) / w
+    t[1, 1] = float(res[0]) / h
+    t[0, 2] = res[1] * (-float(center[0]) / w + .5)
+    t[1, 2] = res[0] * (-float(center[1]) / h + .5)
+    t[2, 2] = 1
+    if not rot == 0:
+        rot = -rot  # To match direction of rotation from cropping
+        rot_mat = np.zeros((3, 3))
+        rot_rad = rot * np.pi / 180
+        sn, cs = np.sin(rot_rad), np.cos(rot_rad)
+        rot_mat[0, :2] = [cs, -sn]
+        rot_mat[1, :2] = [sn, cs]
+        rot_mat[2, 2] = 1
+        # Need to rotate around center
+        t_mat = np.eye(3)
+        t_mat[0, 2] = -res[1] / 2
+        t_mat[1, 2] = -res[0] / 2
+        t_inv = t_mat.copy()
+        t_inv[:2, 2] *= -1
+        t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t)))
+    return t
+
+
+def transform(pt, center, scale, res, invert=0, rot=0):
+    """Transform pixel location to different reference."""
+    t = get_transform(center, scale, res, rot=rot)
+    if invert:
+        t = np.linalg.inv(t)
+    new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T
+    new_pt = np.dot(t, new_pt)
+    return np.array([round(new_pt[0]), round(new_pt[1])], dtype=int) + 1
+
+
+def bbox_from_detector(bbox, input_resolution=(224, 224), rescale=1.25):
+    """
+    Get center and scale of bounding box from bounding box.
+    The expected format is [min_x, min_y, max_x, max_y].
+    """
+    CROP_IMG_HEIGHT, CROP_IMG_WIDTH = input_resolution
+    CROP_ASPECT_RATIO = CROP_IMG_HEIGHT / float(CROP_IMG_WIDTH)
+
+    # center
+    center_x = (bbox[0] + bbox[2]) / 2.0
+    center_y = (bbox[1] + bbox[3]) / 2.0
+    center = np.array([center_x, center_y])
+
+    # scale
+    bbox_w = bbox[2] - bbox[0]
+    bbox_h = bbox[3] - bbox[1]
+    bbox_size = max(bbox_w * CROP_ASPECT_RATIO, bbox_h)
+
+    scale = np.array([bbox_size / CROP_ASPECT_RATIO, bbox_size]) / 200.0
+    # scale = bbox_size / 200.0
+    # adjust bounding box tightness
+    scale *= rescale
+    return center, scale
+
+
+def crop(img, center, scale, res):
+    """
+    Crop image according to the supplied bounding box.
+    res: [rows, cols]
+    """
+    # Upper left point
+    ul = np.array(transform([1, 1], center, max(scale), res, invert=1)) - 1
+    # Bottom right point
+    br = np.array(transform([res[1] + 1, res[0] + 1], center, max(scale), res, invert=1)) - 1
+
+    # Padding so that when rotated proper amount of context is included
+    pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)
+
+    new_shape = [br[1] - ul[1], br[0] - ul[0]]
+    if len(img.shape) > 2:
+        new_shape += [img.shape[2]]
+    new_img = np.zeros(new_shape, dtype=np.float32)
+
+    # Range to fill new array
+    new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0]
+    new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1]
+    # Range to sample from original image
+    old_x = max(0, ul[0]), min(len(img[0]), br[0])
+    old_y = max(0, ul[1]), min(len(img), br[1])
+    try:
+        new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]]
+    except Exception as e:
+        print(e)
+
+    new_img = cv2.resize(new_img, (res[1], res[0]))  # (cols, rows)
+    return new_img, new_shape, (old_x, old_y), (new_x, new_y)  # , ul, br
+
+
+def split_kp2ds_for_aa(kp2ds, ret_face=False):
+    kp2ds_body = (kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2
+    kp2ds_lhand = kp2ds[91:112]
+    kp2ds_rhand = kp2ds[112:133]
+    kp2ds_face = kp2ds[22:91]
+    if ret_face:
+        return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy(), kp2ds_face.copy()
+    return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy()
+
+def load_pose_metas_from_kp2ds_seq_list(kp2ds_seq, width, height):
+    metas = []
+    for kps in kp2ds_seq:
+        if len(kps) != 1:
+            return None
+        kps = kps[0].copy()
+        kps[:, 0] /= width
+        kps[:, 1] /= height
+        kp2ds_body, kp2ds_lhand, kp2ds_rhand, kp2ds_face = split_kp2ds_for_aa(kps, ret_face=True)
+
+        if kp2ds_body[:, :2].min(axis=1).max() < 0:
+            kp2ds_body = last_kp2ds_body
+        last_kp2ds_body = kp2ds_body
+
+        meta = {
+            "width": width,
+            "height": height,
+            "keypoints_body": kp2ds_body.tolist(),
+            "keypoints_left_hand": kp2ds_lhand.tolist(),
+            "keypoints_right_hand": kp2ds_rhand.tolist(),
+            "keypoints_face": kp2ds_face.tolist(),
+        }
+        metas.append(meta)
+    return metas
+
+
+def load_pose_metas_from_kp2ds_seq(kp2ds_seq, width, height):
+    metas = []
+    for kps in kp2ds_seq:
+        kps = kps.copy()
+        kps[:, 0] /= width
+        kps[:, 1] /= height
+        kp2ds_body, kp2ds_lhand, kp2ds_rhand, kp2ds_face = split_kp2ds_for_aa(kps, ret_face=True)
+
+        # 排除全部小于0的情况
+        if kp2ds_body[:, :2].min(axis=1).max() < 0:
+            kp2ds_body = last_kp2ds_body
+        last_kp2ds_body = kp2ds_body
+
+        meta = {
+            "width": width,
+            "height": height,
+            "keypoints_body": kp2ds_body,
+            "keypoints_left_hand": kp2ds_lhand,
+            "keypoints_right_hand": kp2ds_rhand,
+            "keypoints_face": kp2ds_face,
+        }
+        metas.append(meta)
+    return metas
\ No newline at end of file

wan/modules/animate/preprocess/preprocess_data.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import argparse
+from process_pipepline import ProcessPipeline
+
+
+def _parse_args():
+    parser = argparse.ArgumentParser(
+        description="The preprocessing pipeline for Wan-animate."
+    )
+
+    parser.add_argument(
+        "--ckpt_path",
+        type=str,
+        default=None,
+        help="The path to the preprocessing model's checkpoint directory. ")
+
+    parser.add_argument(
+        "--video_path",
+        type=str,
+        default=None,
+        help="The path to the driving video.")
+    parser.add_argument(
+        "--refer_path",
+        type=str,
+        default=None,
+        help="The path to the refererence image.")
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default=None,
+        help="The path to save the processed results.")
+    
+    parser.add_argument(
+        "--resolution_area",
+        type=int,
+        nargs=2,
+        default=[1280, 720],
+        help="The target resolution for processing, specified as [width, height]. To handle different aspect ratios, the video is resized to have a total area equivalent to width * height, while preserving the original aspect ratio."
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=30,
+        help="The target FPS for processing the driving video. Set to -1 to use the video's original FPS."
+    )
+
+    parser.add_argument(
+        "--replace_flag",
+        action="store_true",
+        default=False,
+        help="Whether to use replacement mode.")
+    parser.add_argument(
+        "--retarget_flag",
+        action="store_true",
+        default=False,
+        help="Whether to use pose retargeting. Currently only supported in animation mode")
+    parser.add_argument(
+        "--use_flux",
+        action="store_true",
+        default=False,
+        help="Whether to use image editing in pose retargeting. Recommended if the character in the reference image or the first frame of the driving video is not in a standard, front-facing pose")
+    
+    # Parameters for the mask strategy in replacement mode. These control the mask's size and shape. Refer to https://arxiv.org/pdf/2502.06145
+    parser.add_argument(
+        "--iterations",
+        type=int,
+        default=3,
+        help="Number of iterations for mask dilation."
+    )
+    parser.add_argument(
+        "--k",
+        type=int,
+        default=7,
+        help="Number of kernel size for mask dilation."
+    )
+    parser.add_argument(
+        "--w_len",
+        type=int,
+        default=1,
+        help="The number of subdivisions for the grid along the 'w' dimension. A higher value results in a more detailed contour. A value of 1 means no subdivision is performed."
+    )
+    parser.add_argument(
+        "--h_len",
+        type=int,
+        default=1,
+        help="The number of subdivisions for the grid along the 'h' dimension. A higher value results in a more detailed contour. A value of 1 means no subdivision is performed."
+    )
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = _parse_args()
+    args_dict = vars(args)
+    print(args_dict)
+
+    assert len(args.resolution_area) == 2, "resolution_area should be a list of two integers [width, height]"
+    assert not args.use_flux or args.retarget_flag, "Image editing with FLUX can only be used when pose retargeting is enabled."
+
+    pose2d_checkpoint_path = os.path.join(args.ckpt_path, 'pose2d/vitpose_h_wholebody.onnx')
+    det_checkpoint_path = os.path.join(args.ckpt_path, 'det/yolov10m.onnx')
+
+    sam2_checkpoint_path = os.path.join(args.ckpt_path, 'sam2/sam2_hiera_large.pt') if args.replace_flag else None
+    flux_kontext_path = os.path.join(args.ckpt_path, 'FLUX.1-Kontext-dev') if args.use_flux else None
+    process_pipeline = ProcessPipeline(det_checkpoint_path=det_checkpoint_path, pose2d_checkpoint_path=pose2d_checkpoint_path, sam_checkpoint_path=sam2_checkpoint_path, flux_kontext_path=flux_kontext_path)
+    os.makedirs(args.save_path, exist_ok=True)
+    process_pipeline(video_path=args.video_path, 
+                     refer_image_path=args.refer_path, 
+                     output_path=args.save_path,
+                     resolution_area=args.resolution_area,
+                     fps=args.fps,
+                     iterations=args.iterations,
+                     k=args.k,
+                     w_len=args.w_len,
+                     h_len=args.h_len,
+                     retarget_flag=args.retarget_flag,
+                     use_flux=args.use_flux,
+                     replace_flag=args.replace_flag)
+

wan/modules/animate/preprocess/process_pipepline.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import numpy as np
+import shutil
+import torch
+from diffusers import FluxKontextPipeline
+import cv2
+from loguru import logger
+from PIL import Image
+try:
+    import moviepy.editor as mpy
+except:
+    import moviepy as mpy
+
+from decord import VideoReader
+from pose2d import Pose2d
+from pose2d_utils import AAPoseMeta
+from utils import resize_by_area, get_frame_indices, padding_resize, get_face_bboxes, get_aug_mask, get_mask_body_img
+from human_visualization import draw_aapose_by_meta_new
+from retarget_pose import get_retarget_pose
+import sam2.modeling.sam.transformer as transformer
+transformer.USE_FLASH_ATTN = False
+transformer.MATH_KERNEL_ON = True
+transformer.OLD_GPU = True
+from sam_utils import build_sam2_video_predictor
+
+
+class ProcessPipeline():
+    def __init__(self, det_checkpoint_path, pose2d_checkpoint_path, sam_checkpoint_path, flux_kontext_path):
+        self.pose2d = Pose2d(checkpoint=pose2d_checkpoint_path, detector_checkpoint=det_checkpoint_path)
+
+        model_cfg = "sam2_hiera_l.yaml"
+        if sam_checkpoint_path is not None:
+            self.predictor = build_sam2_video_predictor(model_cfg, sam_checkpoint_path)
+        if flux_kontext_path is not None:
+            self.flux_kontext = FluxKontextPipeline.from_pretrained(flux_kontext_path, torch_dtype=torch.bfloat16).to("cuda")
+
+    def __call__(self, video_path, refer_image_path, output_path, resolution_area=[1280, 720], fps=30, iterations=3, k=7, w_len=1, h_len=1, retarget_flag=False, use_flux=False, replace_flag=False):
+        if replace_flag:
+
+            video_reader = VideoReader(video_path)
+            frame_num = len(video_reader)
+            print('frame_num: {}'.format(frame_num))
+            
+            video_fps = video_reader.get_avg_fps()
+            print('video_fps: {}'.format(video_fps))
+            print('fps: {}'.format(fps))
+
+            # TODO: Maybe we can switch to PyAV later, which can get accurate frame num
+            duration = video_reader.get_frame_timestamp(-1)[-1]      
+            expected_frame_num = int(duration * video_fps + 0.5) 
+            ratio = abs((frame_num - expected_frame_num)/frame_num)         
+            if ratio > 0.1:
+                print("Warning: The difference between the actual number of frames and the expected number of frames is two large")
+                frame_num = expected_frame_num
+
+            if fps == -1:
+                fps = video_fps
+
+            target_num = int(frame_num / video_fps * fps)
+            print('target_num: {}'.format(target_num))
+            idxs = get_frame_indices(frame_num, video_fps, target_num, fps)
+            frames = video_reader.get_batch(idxs).asnumpy()
+
+            frames = [resize_by_area(frame, resolution_area[0] * resolution_area[1], divisor=16) for frame in frames]
+            height, width = frames[0].shape[:2]
+            logger.info(f"Processing pose meta")
+
+
+            tpl_pose_metas = self.pose2d(frames)
+
+            face_images = []
+            for idx, meta in enumerate(tpl_pose_metas):
+                face_bbox_for_image = get_face_bboxes(meta['keypoints_face'][:, :2], scale=1.3,
+                                                    image_shape=(frames[0].shape[0], frames[0].shape[1]))
+
+                x1, x2, y1, y2 = face_bbox_for_image
+                face_image = frames[idx][y1:y2, x1:x2]
+                face_image = cv2.resize(face_image, (512, 512))
+                face_images.append(face_image)
+
+            logger.info(f"Processing reference image: {refer_image_path}")
+            refer_img = cv2.imread(refer_image_path)
+            src_ref_path = os.path.join(output_path, 'src_ref.png')
+            shutil.copy(refer_image_path, src_ref_path)
+            refer_img = refer_img[..., ::-1]
+
+            refer_img = padding_resize(refer_img, height, width)
+            logger.info(f"Processing template video: {video_path}")
+            tpl_retarget_pose_metas = [AAPoseMeta.from_humanapi_meta(meta) for meta in tpl_pose_metas]
+            cond_images = []
+
+            for idx, meta in enumerate(tpl_retarget_pose_metas):
+                canvas = np.zeros_like(refer_img)
+                conditioning_image = draw_aapose_by_meta_new(canvas, meta)
+                cond_images.append(conditioning_image)
+            masks = self.get_mask(frames, 400, tpl_pose_metas)
+
+            bg_images = []
+            aug_masks = []
+
+            for frame, mask in zip(frames, masks):
+                if iterations > 0:
+                    _, each_mask = get_mask_body_img(frame, mask, iterations=iterations, k=k)
+                    each_aug_mask = get_aug_mask(each_mask, w_len=w_len, h_len=h_len)
+                else:
+                    each_aug_mask = mask
+
+                each_bg_image = frame * (1 - each_aug_mask[:, :, None])
+                bg_images.append(each_bg_image)
+                aug_masks.append(each_aug_mask)
+
+            src_face_path = os.path.join(output_path, 'src_face.mp4')
+            mpy.ImageSequenceClip(face_images, fps=fps).write_videofile(src_face_path)
+
+            src_pose_path = os.path.join(output_path, 'src_pose.mp4')
+            mpy.ImageSequenceClip(cond_images, fps=fps).write_videofile(src_pose_path)
+
+            src_bg_path = os.path.join(output_path, 'src_bg.mp4')
+            mpy.ImageSequenceClip(bg_images, fps=fps).write_videofile(src_bg_path)
+
+            aug_masks_new = [np.stack([mask * 255, mask * 255, mask * 255], axis=2) for mask in aug_masks]
+            src_mask_path = os.path.join(output_path, 'src_mask.mp4')
+            mpy.ImageSequenceClip(aug_masks_new, fps=fps).write_videofile(src_mask_path)
+            return True
+        else:
+            logger.info(f"Processing reference image: {refer_image_path}")
+            refer_img = cv2.imread(refer_image_path)
+            src_ref_path = os.path.join(output_path, 'src_ref.png')
+            shutil.copy(refer_image_path, src_ref_path)
+            refer_img = refer_img[..., ::-1]
+            
+            refer_img = resize_by_area(refer_img, resolution_area[0] * resolution_area[1], divisor=16)
+            
+            refer_pose_meta = self.pose2d([refer_img])[0]
+
+
+            logger.info(f"Processing template video: {video_path}")
+            video_reader = VideoReader(video_path)
+            frame_num = len(video_reader)
+            print('frame_num: {}'.format(frame_num))
+
+            video_fps = video_reader.get_avg_fps()
+            print('video_fps: {}'.format(video_fps))
+            print('fps: {}'.format(fps))
+
+            # TODO: Maybe we can switch to PyAV later, which can get accurate frame num
+            duration = video_reader.get_frame_timestamp(-1)[-1]      
+            expected_frame_num = int(duration * video_fps + 0.5) 
+            ratio = abs((frame_num - expected_frame_num)/frame_num)         
+            if ratio > 0.1:
+                print("Warning: The difference between the actual number of frames and the expected number of frames is two large")
+                frame_num = expected_frame_num
+
+            if fps == -1:
+                fps = video_fps
+                
+            target_num = int(frame_num / video_fps * fps)
+            print('target_num: {}'.format(target_num))
+            idxs = get_frame_indices(frame_num, video_fps, target_num, fps)
+            frames = video_reader.get_batch(idxs).asnumpy()
+
+            logger.info(f"Processing pose meta")
+
+            tpl_pose_meta0 = self.pose2d(frames[:1])[0]
+            tpl_pose_metas = self.pose2d(frames)
+
+            face_images = []
+            for idx, meta in enumerate(tpl_pose_metas):
+                face_bbox_for_image = get_face_bboxes(meta['keypoints_face'][:, :2], scale=1.3,
+                                                    image_shape=(frames[0].shape[0], frames[0].shape[1]))
+
+                x1, x2, y1, y2 = face_bbox_for_image
+                face_image = frames[idx][y1:y2, x1:x2]
+                face_image = cv2.resize(face_image, (512, 512))
+                face_images.append(face_image)
+
+            if retarget_flag:
+                if use_flux:
+                    tpl_prompt, refer_prompt = self.get_editing_prompts(tpl_pose_metas, refer_pose_meta)
+                    refer_input = Image.fromarray(refer_img)
+                    refer_edit = self.flux_kontext(
+                            image=refer_input,
+                            height=refer_img.shape[0],
+                            width=refer_img.shape[1],
+                            prompt=refer_prompt,
+                            guidance_scale=2.5,
+                            num_inference_steps=28,
+                        ).images[0]
+                    
+                    refer_edit = Image.fromarray(padding_resize(np.array(refer_edit), refer_img.shape[0], refer_img.shape[1]))
+                    refer_edit_path = os.path.join(output_path, 'refer_edit.png')
+                    refer_edit.save(refer_edit_path)
+                    refer_edit_pose_meta = self.pose2d([np.array(refer_edit)])[0]
+
+                    tpl_img = frames[1]
+                    tpl_input = Image.fromarray(tpl_img)
+                    
+                    tpl_edit = self.flux_kontext(
+                            image=tpl_input,
+                            height=tpl_img.shape[0],
+                            width=tpl_img.shape[1],
+                            prompt=tpl_prompt,
+                            guidance_scale=2.5,
+                            num_inference_steps=28,
+                        ).images[0]
+                    
+                    tpl_edit = Image.fromarray(padding_resize(np.array(tpl_edit), tpl_img.shape[0], tpl_img.shape[1]))
+                    tpl_edit_path = os.path.join(output_path, 'tpl_edit.png')
+                    tpl_edit.save(tpl_edit_path)
+                    tpl_edit_pose_meta0 = self.pose2d([np.array(tpl_edit)])[0]
+                    tpl_retarget_pose_metas = get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, tpl_edit_pose_meta0, refer_edit_pose_meta)
+                else:
+                    tpl_retarget_pose_metas = get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, None, None)
+            else:
+               tpl_retarget_pose_metas = [AAPoseMeta.from_humanapi_meta(meta) for meta in tpl_pose_metas]
+
+            cond_images = []
+            for idx, meta in enumerate(tpl_retarget_pose_metas):
+                if retarget_flag:
+                    canvas = np.zeros_like(refer_img)
+                    conditioning_image = draw_aapose_by_meta_new(canvas, meta)
+                else:
+                    canvas = np.zeros_like(frames[0])
+                    conditioning_image = draw_aapose_by_meta_new(canvas, meta)
+                    conditioning_image = padding_resize(conditioning_image, refer_img.shape[0], refer_img.shape[1])
+
+                cond_images.append(conditioning_image)
+
+            src_face_path = os.path.join(output_path, 'src_face.mp4')
+            mpy.ImageSequenceClip(face_images, fps=fps).write_videofile(src_face_path)
+
+            src_pose_path = os.path.join(output_path, 'src_pose.mp4')
+            mpy.ImageSequenceClip(cond_images, fps=fps).write_videofile(src_pose_path)
+            return True
+
+    def get_editing_prompts(self, tpl_pose_metas, refer_pose_meta):
+        arm_visible = False
+        leg_visible = False
+        for tpl_pose_meta in tpl_pose_metas:
+            tpl_keypoints = tpl_pose_meta['keypoints_body']
+            if tpl_keypoints[3].all() != 0 or tpl_keypoints[4].all() != 0 or tpl_keypoints[6].all() != 0 or tpl_keypoints[7].all() != 0:
+                if (tpl_keypoints[3][0] <= 1 and tpl_keypoints[3][1] <= 1 and tpl_keypoints[3][2] >= 0.75) or (tpl_keypoints[4][0] <= 1 and tpl_keypoints[4][1] <= 1 and tpl_keypoints[4][2] >= 0.75) or \
+                    (tpl_keypoints[6][0] <= 1 and tpl_keypoints[6][1] <= 1 and tpl_keypoints[6][2] >= 0.75) or (tpl_keypoints[7][0] <= 1 and tpl_keypoints[7][1] <= 1 and tpl_keypoints[7][2] >= 0.75):
+                    arm_visible = True
+            if tpl_keypoints[9].all() != 0 or tpl_keypoints[12].all() != 0 or tpl_keypoints[10].all() != 0 or tpl_keypoints[13].all() != 0:
+                if (tpl_keypoints[9][0] <= 1 and tpl_keypoints[9][1] <= 1 and tpl_keypoints[9][2] >= 0.75) or (tpl_keypoints[12][0] <= 1 and tpl_keypoints[12][1] <= 1 and tpl_keypoints[12][2] >= 0.75) or \
+                    (tpl_keypoints[10][0] <= 1 and tpl_keypoints[10][1] <= 1 and tpl_keypoints[10][2] >= 0.75) or (tpl_keypoints[13][0] <= 1 and tpl_keypoints[13][1] <= 1 and tpl_keypoints[13][2] >= 0.75):
+                    leg_visible = True
+            if arm_visible and leg_visible:
+                break
+        
+        if leg_visible:
+            if tpl_pose_meta['width'] > tpl_pose_meta['height']:
+                tpl_prompt = "Change the person to a standard T-pose (facing forward with arms extended). The person is standing. Feet and Hands are visible in the image."
+            else:
+                tpl_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. The person is standing. Feet and Hands are visible in the image."
+
+            if refer_pose_meta['width'] > refer_pose_meta['height']:
+                refer_prompt = "Change the person to a standard T-pose (facing forward with arms extended). The person is standing. Feet and Hands are visible in the image."
+            else:
+                refer_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. The person is standing. Feet and Hands are visible in the image."
+        elif arm_visible:
+            if tpl_pose_meta['width'] > tpl_pose_meta['height']:
+                tpl_prompt = "Change the person to a standard T-pose (facing forward with arms extended). Hands are visible in the image."
+            else:
+                tpl_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. Hands are visible in the image."
+
+            if refer_pose_meta['width'] > refer_pose_meta['height']:
+                refer_prompt = "Change the person to a standard T-pose (facing forward with arms extended). Hands are visible in the image."
+            else:
+                refer_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. Hands are visible in the image."
+        else:
+            tpl_prompt = "Change the person to face forward."
+            refer_prompt = "Change the person to face forward."
+
+        return tpl_prompt, refer_prompt
+    
+
+    def get_mask(self, frames, th_step, kp2ds_all):
+        frame_num = len(frames)
+        if frame_num < th_step:
+            num_step = 1
+        else:
+            num_step = (frame_num + th_step) // th_step
+
+        all_mask = []
+        for index in range(num_step):
+            each_frames = frames[index * th_step:(index + 1) * th_step]
+    
+            kp2ds = kp2ds_all[index * th_step:(index + 1) * th_step]
+            if len(each_frames) > 4:
+                key_frame_num = 4
+            elif 4 >= len(each_frames) > 0:
+                key_frame_num = 1
+            else:
+                continue
+
+            key_frame_step = len(kp2ds) // key_frame_num
+            key_frame_index_list = list(range(0, len(kp2ds), key_frame_step))
+
+            key_points_index = [0, 1, 2, 5, 8, 11, 10, 13]
+            key_frame_body_points_list = []
+            for key_frame_index in key_frame_index_list:
+                keypoints_body_list = []
+                body_key_points = kp2ds[key_frame_index]['keypoints_body']
+                for each_index in key_points_index:
+                    each_keypoint = body_key_points[each_index]
+                    if None is each_keypoint:
+                        continue
+                    keypoints_body_list.append(each_keypoint)
+
+                keypoints_body = np.array(keypoints_body_list)[:, :2]
+                wh = np.array([[kp2ds[0]['width'], kp2ds[0]['height']]])
+                points = (keypoints_body * wh).astype(np.int32)
+                key_frame_body_points_list.append(points)
+
+            inference_state = self.predictor.init_state_v2(frames=each_frames)
+            self.predictor.reset_state(inference_state)
+            ann_obj_id = 1
+            for ann_frame_idx, points in zip(key_frame_index_list, key_frame_body_points_list):
+                labels = np.array([1] * points.shape[0], np.int32)
+                _, out_obj_ids, out_mask_logits = self.predictor.add_new_points(
+                    inference_state=inference_state,
+                    frame_idx=ann_frame_idx,
+                    obj_id=ann_obj_id,
+                    points=points,
+                    labels=labels,
+                )
+
+            video_segments = {}
+            for out_frame_idx, out_obj_ids, out_mask_logits in self.predictor.propagate_in_video(inference_state):
+                video_segments[out_frame_idx] = {
+                    out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
+                    for i, out_obj_id in enumerate(out_obj_ids)
+                }
+
+            for out_frame_idx in range(len(video_segments)):
+                for out_obj_id, out_mask in video_segments[out_frame_idx].items():
+                    out_mask = out_mask[0].astype(np.uint8)
+                    all_mask.append(out_mask)
+
+        return all_mask
+    
+    def convert_list_to_array(self, metas):
+        metas_list = []
+        for meta in metas:
+            for key, value in meta.items():
+                if type(value) is list:
+                    value = np.array(value)
+                meta[key] = value
+            metas_list.append(meta)
+        return metas_list
+

wan/modules/animate/preprocess/retarget_pose.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import cv2
+import numpy as np
+import json
+from tqdm import tqdm
+import math 
+from typing import NamedTuple, List
+import copy
+from pose2d_utils import AAPoseMeta
+
+
+# load skeleton name and bone lines
+keypoint_list = [
+        "Nose",
+        "Neck",
+        "RShoulder",
+        "RElbow",
+        "RWrist", # No.4
+        "LShoulder",
+        "LElbow",
+        "LWrist", # No.7
+        "RHip",
+        "RKnee",
+        "RAnkle", # No.10
+        "LHip",
+        "LKnee",
+        "LAnkle", # No.13
+        "REye",
+        "LEye",
+        "REar",
+        "LEar",
+        "LToe",
+        "RToe",
+]
+
+
+limbSeq = [
+    [2, 3], [2, 6],     # shoulders
+    [3, 4], [4, 5],     # left arm
+    [6, 7], [7, 8],     # right arm
+    [2, 9], [9, 10], [10, 11],    # right leg 
+    [2, 12], [12, 13], [13, 14],  # left leg
+    [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears)
+    [14, 19], # left foot
+    [11, 20] #  right foot
+]
+
+eps = 0.01
+
+class Keypoint(NamedTuple):
+    x: float
+    y: float
+    score: float = 1.0
+    id: int = -1
+
+
+# for each limb, calculate src & dst bone's length
+# and calculate their ratios 
+def get_length(skeleton, limb):
+    
+    k1_index, k2_index = limb
+    
+    H, W = skeleton['height'], skeleton['width']
+    keypoints = skeleton['keypoints_body']
+    keypoint1 = keypoints[k1_index - 1]
+    keypoint2 = keypoints[k2_index - 1]
+
+    if keypoint1 is None or keypoint2 is None:
+        return None, None, None
+    
+    X = np.array([keypoint1[0], keypoint2[0]]) * float(W)
+    Y = np.array([keypoint1[1], keypoint2[1]]) * float(H)
+    length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+    
+    return X, Y, length
+
+
+
+def get_handpose_meta(keypoints, delta, src_H, src_W):
+
+    new_keypoints = []
+
+    for idx, keypoint in enumerate(keypoints):
+        if keypoint is None:
+            new_keypoints.append(None)
+            continue
+        if keypoint.score == 0:
+            new_keypoints.append(None)
+            continue
+
+        x, y = keypoint.x, keypoint.y
+        x = int(x * src_W + delta[0])
+        y = int(y * src_H + delta[1])
+
+        new_keypoints.append(                
+                Keypoint(
+                    x=x,
+                    y=y,
+                    score=keypoint.score,
+                ))
+
+    return new_keypoints
+
+
+def deal_hand_keypoints(hand_res, r_ratio, l_ratio, hand_score_th = 0.5):
+
+    left_hand = []
+    right_hand = []
+
+    left_delta_x = hand_res['left'][0][0] * (l_ratio - 1) 
+    left_delta_y = hand_res['left'][0][1] * (l_ratio - 1)
+
+    right_delta_x = hand_res['right'][0][0] * (r_ratio - 1)
+    right_delta_y = hand_res['right'][0][1] * (r_ratio - 1)
+
+    length = len(hand_res['left'])
+
+    for i in range(length):
+        # left hand
+        if hand_res['left'][i][2] < hand_score_th:
+            left_hand.append(
+                Keypoint(
+                    x=-1,
+                    y=-1,
+                    score=0,
+                )
+            )
+        else:
+            left_hand.append(
+                Keypoint(
+                    x=hand_res['left'][i][0] * l_ratio - left_delta_x,
+                    y=hand_res['left'][i][1] * l_ratio - left_delta_y,
+                    score = hand_res['left'][i][2]
+                )
+            )
+
+        # right hand
+        if hand_res['right'][i][2] < hand_score_th:
+            right_hand.append(
+                Keypoint(
+                    x=-1,
+                    y=-1,
+                    score=0,
+                )
+            )
+        else:
+            right_hand.append(
+                Keypoint(
+                    x=hand_res['right'][i][0] * r_ratio - right_delta_x,
+                    y=hand_res['right'][i][1] * r_ratio - right_delta_y,
+                    score = hand_res['right'][i][2]
+                )
+            )
+
+    return right_hand, left_hand
+
+
+def get_scaled_pose(canvas, src_canvas, keypoints, keypoints_hand, bone_ratio_list, delta_ground_x, delta_ground_y,
+                                       rescaled_src_ground_x, body_flag, id, scale_min, threshold = 0.4):
+
+    H, W = canvas
+    src_H, src_W = src_canvas
+
+    new_length_list = [ ] 
+    angle_list = [ ]
+
+    # keypoints from 0-1 to H/W range
+    for idx in range(len(keypoints)):
+        if keypoints[idx] is None or len(keypoints[idx]) == 0:
+            continue
+
+        keypoints[idx] = [keypoints[idx][0] * src_W, keypoints[idx][1] * src_H, keypoints[idx][2]]
+
+    # first traverse, get new_length_list and angle_list
+    for idx, (k1_index, k2_index) in enumerate(limbSeq):
+        keypoint1 = keypoints[k1_index - 1]
+        keypoint2 = keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None or len(keypoint1) == 0 or len(keypoint2) == 0:
+            new_length_list.append(None)
+            angle_list.append(None)
+            continue
+
+        Y = np.array([keypoint1[0], keypoint2[0]]) #* float(W)
+        X = np.array([keypoint1[1], keypoint2[1]]) #* float(H)
+
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+
+        new_length = length * bone_ratio_list[idx]
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+
+        new_length_list.append(new_length)
+        angle_list.append(angle)
+
+    # Keep foot length within 0.5x calf length
+    foot_lower_leg_ratio = 0.5
+    if new_length_list[8] != None and new_length_list[18] != None:
+        if new_length_list[18] > new_length_list[8] * foot_lower_leg_ratio:
+            new_length_list[18] = new_length_list[8] * foot_lower_leg_ratio
+
+    if new_length_list[11] != None and new_length_list[17] != None:
+        if new_length_list[17] > new_length_list[11] * foot_lower_leg_ratio:
+            new_length_list[17] = new_length_list[11] * foot_lower_leg_ratio
+
+    # second traverse, calculate new keypoints
+    rescale_keypoints = keypoints.copy()
+
+    for idx, (k1_index, k2_index) in enumerate(limbSeq):
+        # update dst_keypoints
+        start_keypoint = rescale_keypoints[k1_index - 1]
+        new_length = new_length_list[idx]
+        angle = angle_list[idx]
+
+        if rescale_keypoints[k1_index - 1] is None or rescale_keypoints[k2_index - 1] is None or \
+            len(rescale_keypoints[k1_index - 1]) == 0 or len(rescale_keypoints[k2_index - 1]) == 0:
+            continue
+
+        # calculate end_keypoint
+        delta_x = new_length * math.cos(math.radians(angle))
+        delta_y = new_length * math.sin(math.radians(angle))
+        
+        end_keypoint_x = start_keypoint[0] - delta_x
+        end_keypoint_y = start_keypoint[1] - delta_y
+
+        # update keypoints
+        rescale_keypoints[k2_index - 1] = [end_keypoint_x, end_keypoint_y, rescale_keypoints[k2_index - 1][2]]
+
+    if id == 0:
+        if body_flag == 'full_body' and rescale_keypoints[8] != None and rescale_keypoints[11] != None:
+            delta_ground_x_offset_first_frame = (rescale_keypoints[8][0] + rescale_keypoints[11][0]) / 2 - rescaled_src_ground_x
+            delta_ground_x += delta_ground_x_offset_first_frame
+        elif body_flag == 'half_body' and rescale_keypoints[1] != None:
+            delta_ground_x_offset_first_frame = rescale_keypoints[1][0] - rescaled_src_ground_x
+            delta_ground_x += delta_ground_x_offset_first_frame
+
+    # offset all keypoints
+    for idx in range(len(rescale_keypoints)):
+        if rescale_keypoints[idx] is None or len(rescale_keypoints[idx]) == 0 :
+            continue
+        rescale_keypoints[idx][0] -= delta_ground_x
+        rescale_keypoints[idx][1] -= delta_ground_y
+
+        # rescale keypoints to original size
+        rescale_keypoints[idx][0] /= scale_min
+        rescale_keypoints[idx][1] /= scale_min
+
+    # Scale hand proportions based on body skeletal ratios
+    r_ratio = max(bone_ratio_list[0], bone_ratio_list[1]) / scale_min
+    l_ratio = max(bone_ratio_list[0], bone_ratio_list[1]) / scale_min
+    left_hand, right_hand = deal_hand_keypoints(keypoints_hand, r_ratio, l_ratio, hand_score_th = threshold)
+
+    left_hand_new = left_hand.copy()
+    right_hand_new = right_hand.copy()
+
+    if rescale_keypoints[4] == None and rescale_keypoints[7] == None:
+        pass
+
+    elif rescale_keypoints[4] == None and rescale_keypoints[7] != None:
+        right_hand_delta =  np.array(rescale_keypoints[7][:2]) - np.array(keypoints[7][:2])
+        right_hand_new = get_handpose_meta(right_hand, right_hand_delta, src_H, src_W)
+
+    elif rescale_keypoints[4] != None and rescale_keypoints[7] == None:
+        left_hand_delta = np.array(rescale_keypoints[4][:2]) - np.array(keypoints[4][:2]) 
+        left_hand_new = get_handpose_meta(left_hand, left_hand_delta, src_H, src_W)
+
+    else:
+        # get left_hand and right_hand offset 
+        left_hand_delta = np.array(rescale_keypoints[4][:2]) - np.array(keypoints[4][:2]) 
+        right_hand_delta =  np.array(rescale_keypoints[7][:2]) - np.array(keypoints[7][:2])
+
+        if keypoints[4][0] != None and left_hand[0].x != -1:
+            left_hand_root_offset = np.array( ( keypoints[4][0] - left_hand[0].x * src_W,  keypoints[4][1] - left_hand[0].y * src_H))  
+            left_hand_delta += left_hand_root_offset
+
+        if keypoints[7][0] != None and right_hand[0].x != -1:
+            right_hand_root_offset = np.array( ( keypoints[7][0] - right_hand[0].x * src_W, keypoints[7][1] - right_hand[0].y * src_H))  
+            right_hand_delta += right_hand_root_offset
+
+        dis_left_hand = ((keypoints[4][0] - left_hand[0].x * src_W) ** 2 + (keypoints[4][1] - left_hand[0].y * src_H) ** 2) ** 0.5
+        dis_right_hand = ((keypoints[7][0] - left_hand[0].x * src_W) ** 2 + (keypoints[7][1] - left_hand[0].y * src_H) ** 2) ** 0.5
+
+        if dis_left_hand > dis_right_hand: 
+            right_hand_new = get_handpose_meta(left_hand, right_hand_delta, src_H, src_W)
+            left_hand_new = get_handpose_meta(right_hand, left_hand_delta, src_H, src_W)
+        else:
+            left_hand_new = get_handpose_meta(left_hand, left_hand_delta, src_H, src_W)
+            right_hand_new = get_handpose_meta(right_hand, right_hand_delta, src_H, src_W)
+
+    # get normalized keypoints_body
+    norm_body_keypoints = [ ]
+    for body_keypoint in rescale_keypoints:
+        if body_keypoint != None:
+            norm_body_keypoints.append([body_keypoint[0] / W , body_keypoint[1] / H, body_keypoint[2]])
+        else:
+            norm_body_keypoints.append(None)
+
+    frame_info = {
+                    'height': H,
+                    'width': W,
+                    'keypoints_body': norm_body_keypoints,
+                    'keypoints_left_hand' : left_hand_new,
+                    'keypoints_right_hand' : right_hand_new,
+                }
+
+    return frame_info
+
+
+def rescale_skeleton(H, W, keypoints, bone_ratio_list):
+
+    rescale_keypoints = keypoints.copy()
+
+    new_length_list = [ ] 
+    angle_list = [ ]
+
+    # keypoints from 0-1 to H/W range
+    for idx in range(len(rescale_keypoints)):
+        if rescale_keypoints[idx] is None or len(rescale_keypoints[idx]) == 0:
+            continue
+
+        rescale_keypoints[idx] = [rescale_keypoints[idx][0] * W, rescale_keypoints[idx][1] * H]
+
+    # first traverse, get new_length_list and angle_list
+    for idx, (k1_index, k2_index) in enumerate(limbSeq):
+        keypoint1 = rescale_keypoints[k1_index - 1]
+        keypoint2 = rescale_keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None or len(keypoint1) == 0 or len(keypoint2) == 0:
+            new_length_list.append(None)
+            angle_list.append(None)
+            continue
+
+        Y = np.array([keypoint1[0], keypoint2[0]]) #* float(W)
+        X = np.array([keypoint1[1], keypoint2[1]]) #* float(H)
+
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+
+
+        new_length = length * bone_ratio_list[idx]
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+
+        new_length_list.append(new_length)
+        angle_list.append(angle)
+
+    # # second traverse, calculate new keypoints
+    for idx, (k1_index, k2_index) in enumerate(limbSeq):
+        # update dst_keypoints
+        start_keypoint = rescale_keypoints[k1_index - 1]
+        new_length = new_length_list[idx]
+        angle = angle_list[idx]
+
+        if rescale_keypoints[k1_index - 1] is None or rescale_keypoints[k2_index - 1] is None or \
+            len(rescale_keypoints[k1_index - 1]) == 0 or len(rescale_keypoints[k2_index - 1]) == 0:
+            continue
+
+        # calculate end_keypoint
+        delta_x = new_length * math.cos(math.radians(angle))
+        delta_y = new_length * math.sin(math.radians(angle))
+        
+        end_keypoint_x = start_keypoint[0] - delta_x
+        end_keypoint_y = start_keypoint[1] - delta_y
+
+        # update keypoints
+        rescale_keypoints[k2_index - 1] = [end_keypoint_x, end_keypoint_y]
+
+    return rescale_keypoints
+
+
+def fix_lack_keypoints_use_sym(skeleton):
+
+    keypoints = skeleton['keypoints_body']
+    H, W = skeleton['height'], skeleton['width']
+
+    limb_points_list = [
+                        [3, 4, 5],
+                        [6, 7, 8],
+                        [12, 13, 14, 19],
+                        [9, 10, 11, 20],
+    ]
+
+    for limb_points in limb_points_list:
+        miss_flag = False
+        for point in limb_points:
+            if keypoints[point - 1] is None:
+                miss_flag = True
+                continue
+            if miss_flag:
+                skeleton['keypoints_body'][point - 1] = None
+
+    repair_limb_seq_left = [
+        [3, 4], [4, 5],     # left arm
+        [12, 13], [13, 14],  # left leg
+        [14, 19] # left foot
+    ]
+
+    repair_limb_seq_right = [
+        [6, 7], [7, 8],     # right arm
+        [9, 10], [10, 11],    # right leg 
+        [11, 20] # right foot
+    ]
+
+    repair_limb_seq = [repair_limb_seq_left, repair_limb_seq_right]
+
+    for idx_part, part in enumerate(repair_limb_seq):
+        for idx, limb in enumerate(part):
+
+            k1_index, k2_index = limb
+            keypoint1 = keypoints[k1_index - 1]
+            keypoint2 = keypoints[k2_index - 1]
+
+            if keypoint1 != None and keypoint2 is None:
+                # reference to symmetric limb
+                sym_limb = repair_limb_seq[1-idx_part][idx]
+                k1_index_sym, k2_index_sym = sym_limb
+                keypoint1_sym = keypoints[k1_index_sym - 1]
+                keypoint2_sym = keypoints[k2_index_sym - 1]
+                ref_length = 0
+
+                if keypoint1_sym != None and keypoint2_sym != None:
+                    X = np.array([keypoint1_sym[0], keypoint2_sym[0]]) * float(W)
+                    Y = np.array([keypoint1_sym[1], keypoint2_sym[1]]) * float(H)
+                    ref_length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+                else:
+                    ref_length_left, ref_length_right = 0, 0
+                    if keypoints[1] != None and keypoints[8] != None:
+                        X = np.array([keypoints[1][0], keypoints[8][0]]) * float(W)
+                        Y = np.array([keypoints[1][1], keypoints[8][1]]) * float(H)
+                        ref_length_left = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+                        if idx <= 1: # arms
+                            ref_length_left /= 2
+                    
+                    if keypoints[1] != None and keypoints[11] != None:
+                        X = np.array([keypoints[1][0], keypoints[11][0]]) * float(W)
+                        Y = np.array([keypoints[1][1], keypoints[11][1]]) * float(H)
+                        ref_length_right = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+                        if idx <= 1: # arms
+                            ref_length_right /= 2
+                        elif idx == 4: # foot
+                            ref_length_right /= 5
+
+                    ref_length = max(ref_length_left, ref_length_right)
+                    
+                if ref_length != 0:
+                    skeleton['keypoints_body'][k2_index - 1] = [0, 0] #init
+                    skeleton['keypoints_body'][k2_index - 1][0] = skeleton['keypoints_body'][k1_index - 1][0]
+                    skeleton['keypoints_body'][k2_index - 1][1] = skeleton['keypoints_body'][k1_index - 1][1] + ref_length / H
+    return skeleton
+
+
+def rescale_shorten_skeleton(ratio_list, src_length_list, dst_length_list):
+
+    modify_bone_list = [
+        [0, 1],
+        [2, 4],
+        [3, 5],
+        [6, 9],
+        [7, 10],
+        [8, 11],
+        [17, 18]
+    ]
+
+    for modify_bone in modify_bone_list:
+        new_ratio = max(ratio_list[modify_bone[0]], ratio_list[modify_bone[1]])
+        ratio_list[modify_bone[0]] = new_ratio
+        ratio_list[modify_bone[1]] = new_ratio
+    
+    if ratio_list[13]!= None and ratio_list[15]!= None:
+        ratio_eye_avg = (ratio_list[13] + ratio_list[15]) / 2
+        ratio_list[13] = ratio_eye_avg
+        ratio_list[15] = ratio_eye_avg
+
+    if ratio_list[14]!= None and ratio_list[16]!= None:
+        ratio_eye_avg = (ratio_list[14] + ratio_list[16]) / 2
+        ratio_list[14] = ratio_eye_avg
+        ratio_list[16] = ratio_eye_avg
+
+    return ratio_list, src_length_list, dst_length_list
+
+
+
+def check_full_body(keypoints, threshold = 0.4):
+
+    body_flag = 'half_body'
+
+    # 1. If ankle points exist, confidence is greater than the threshold, and points do not exceed the frame, return full_body
+    if keypoints[10] != None and keypoints[13] != None and keypoints[8] != None and keypoints[11] != None:
+        if (keypoints[10][1] <= 1 and keypoints[13][1] <= 1) and (keypoints[10][2] >= threshold and keypoints[13][2] >= threshold) and \
+            (keypoints[8][1] <= 1 and keypoints[11][1] <= 1) and (keypoints[8][2] >= threshold and keypoints[11][2] >= threshold):
+            body_flag = 'full_body'
+            return body_flag
+
+    # 2. If hip points exist, return three_quarter_body
+    if (keypoints[8] != None and keypoints[11] != None):
+        if (keypoints[8][1] <= 1 and keypoints[11][1] <= 1) and (keypoints[8][2] >= threshold and keypoints[11][2] >= threshold):
+            body_flag = 'three_quarter_body'
+            return body_flag
+    
+    return body_flag
+
+
+def check_full_body_both(flag1, flag2):
+    body_flag_dict = {
+        'full_body': 2,
+        'three_quarter_body' : 1,
+        'half_body': 0
+    }
+
+    body_flag_dict_reverse = {
+        2: 'full_body', 
+        1: 'three_quarter_body',
+        0: 'half_body'
+    }
+
+    flag1_num = body_flag_dict[flag1]
+    flag2_num = body_flag_dict[flag2]
+    flag_both_num = min(flag1_num, flag2_num)
+    return body_flag_dict_reverse[flag_both_num]
+
+
+def write_to_poses(data_to_json, none_idx, dst_shape, bone_ratio_list, delta_ground_x, delta_ground_y, rescaled_src_ground_x, body_flag, scale_min):
+    outputs = []
+    length = len(data_to_json)
+    for id in tqdm(range(length)):
+
+        src_height, src_width = data_to_json[id]['height'], data_to_json[id]['width']
+        width, height = dst_shape
+        keypoints = data_to_json[id]['keypoints_body']
+        for idx in range(len(keypoints)):
+            if idx in none_idx:
+                keypoints[idx] = None
+        new_keypoints = keypoints.copy()
+
+        # get hand keypoints
+        keypoints_hand = {'left' : data_to_json[id]['keypoints_left_hand'], 'right' : data_to_json[id]['keypoints_right_hand']}
+        # Normalize hand coordinates to 0-1 range
+        for hand_idx in range(len(data_to_json[id]['keypoints_left_hand'])):
+            data_to_json[id]['keypoints_left_hand'][hand_idx][0] = data_to_json[id]['keypoints_left_hand'][hand_idx][0] / src_width
+            data_to_json[id]['keypoints_left_hand'][hand_idx][1] = data_to_json[id]['keypoints_left_hand'][hand_idx][1] / src_height
+
+        for hand_idx in range(len(data_to_json[id]['keypoints_right_hand'])):
+            data_to_json[id]['keypoints_right_hand'][hand_idx][0] = data_to_json[id]['keypoints_right_hand'][hand_idx][0] / src_width
+            data_to_json[id]['keypoints_right_hand'][hand_idx][1] = data_to_json[id]['keypoints_right_hand'][hand_idx][1] / src_height
+
+        
+        frame_info = get_scaled_pose((height, width), (src_height, src_width), new_keypoints, keypoints_hand, bone_ratio_list, delta_ground_x, delta_ground_y, rescaled_src_ground_x, body_flag, id, scale_min)
+        outputs.append(frame_info)
+
+    return outputs
+
+
+def calculate_scale_ratio(skeleton, skeleton_edit, scale_ratio_flag):
+    if scale_ratio_flag:
+
+        headw = max(skeleton['keypoints_body'][0][0], skeleton['keypoints_body'][14][0], skeleton['keypoints_body'][15][0], skeleton['keypoints_body'][16][0], skeleton['keypoints_body'][17][0]) - \
+                    min(skeleton['keypoints_body'][0][0], skeleton['keypoints_body'][14][0], skeleton['keypoints_body'][15][0], skeleton['keypoints_body'][16][0], skeleton['keypoints_body'][17][0])
+        headw_edit = max(skeleton_edit['keypoints_body'][0][0], skeleton_edit['keypoints_body'][14][0], skeleton_edit['keypoints_body'][15][0], skeleton_edit['keypoints_body'][16][0], skeleton_edit['keypoints_body'][17][0]) - \
+                    min(skeleton_edit['keypoints_body'][0][0], skeleton_edit['keypoints_body'][14][0], skeleton_edit['keypoints_body'][15][0], skeleton_edit['keypoints_body'][16][0], skeleton_edit['keypoints_body'][17][0])
+        headw_ratio = headw / headw_edit
+
+        _, _, shoulder = get_length(skeleton, [6,3])
+        _, _, shoulder_edit = get_length(skeleton_edit, [6,3])
+        shoulder_ratio = shoulder / shoulder_edit
+
+        return max(headw_ratio, shoulder_ratio)
+    
+    else:
+        return 1
+
+
+
+def retarget_pose(src_skeleton, dst_skeleton, all_src_skeleton, src_skeleton_edit, dst_skeleton_edit, threshold=0.4):
+
+    if src_skeleton_edit is not None and dst_skeleton_edit is not None:
+        use_edit_for_base = True
+    else:
+        use_edit_for_base = False
+
+    src_skeleton_ori = copy.deepcopy(src_skeleton)
+
+    dst_skeleton_ori_h, dst_skeleton_ori_w = dst_skeleton['height'], dst_skeleton['width']
+    if src_skeleton['keypoints_body'][0] != None and src_skeleton['keypoints_body'][10] != None and src_skeleton['keypoints_body'][13] != None and \
+        dst_skeleton['keypoints_body'][0] != None and dst_skeleton['keypoints_body'][10] != None and dst_skeleton['keypoints_body'][13] != None and \
+            src_skeleton['keypoints_body'][0][2] > 0.5 and src_skeleton['keypoints_body'][10][2] > 0.5 and src_skeleton['keypoints_body'][13][2] > 0.5 and \
+        dst_skeleton['keypoints_body'][0][2] > 0.5 and dst_skeleton['keypoints_body'][10][2] > 0.5 and dst_skeleton['keypoints_body'][13][2] > 0.5:
+
+        src_height = src_skeleton['height'] * abs(
+            (src_skeleton['keypoints_body'][10][1] + src_skeleton['keypoints_body'][13][1]) / 2 -
+            src_skeleton['keypoints_body'][0][1])
+        dst_height = dst_skeleton['height'] * abs(
+            (dst_skeleton['keypoints_body'][10][1] + dst_skeleton['keypoints_body'][13][1]) / 2 -
+            dst_skeleton['keypoints_body'][0][1])
+        scale_min = 1.0 * src_height / dst_height
+    elif src_skeleton['keypoints_body'][0] != None and src_skeleton['keypoints_body'][8] != None and src_skeleton['keypoints_body'][11] != None and \
+        dst_skeleton['keypoints_body'][0] != None and dst_skeleton['keypoints_body'][8] != None and dst_skeleton['keypoints_body'][11] != None and \
+            src_skeleton['keypoints_body'][0][2] > 0.5 and src_skeleton['keypoints_body'][8][2] > 0.5 and src_skeleton['keypoints_body'][11][2] > 0.5 and \
+        dst_skeleton['keypoints_body'][0][2] > 0.5 and dst_skeleton['keypoints_body'][8][2] > 0.5 and dst_skeleton['keypoints_body'][11][2] > 0.5:
+
+        src_height = src_skeleton['height'] * abs(
+            (src_skeleton['keypoints_body'][8][1] + src_skeleton['keypoints_body'][11][1]) / 2 -
+            src_skeleton['keypoints_body'][0][1])
+        dst_height = dst_skeleton['height'] * abs(
+            (dst_skeleton['keypoints_body'][8][1] + dst_skeleton['keypoints_body'][11][1]) / 2 -
+            dst_skeleton['keypoints_body'][0][1])
+        scale_min = 1.0 * src_height / dst_height
+    else:
+        scale_min = np.sqrt(src_skeleton['height'] * src_skeleton['width']) / np.sqrt(dst_skeleton['height'] * dst_skeleton['width'])
+    
+    if use_edit_for_base:
+        scale_ratio_flag = False
+        if src_skeleton_edit['keypoints_body'][0] != None and src_skeleton_edit['keypoints_body'][10] != None and src_skeleton_edit['keypoints_body'][13] != None and \
+            dst_skeleton_edit['keypoints_body'][0] != None and dst_skeleton_edit['keypoints_body'][10] != None and dst_skeleton_edit['keypoints_body'][13] != None and \
+                src_skeleton_edit['keypoints_body'][0][2] > 0.5 and src_skeleton_edit['keypoints_body'][10][2] > 0.5 and src_skeleton_edit['keypoints_body'][13][2] > 0.5 and \
+            dst_skeleton_edit['keypoints_body'][0][2] > 0.5 and dst_skeleton_edit['keypoints_body'][10][2] > 0.5 and dst_skeleton_edit['keypoints_body'][13][2] > 0.5:
+
+            src_height_edit = src_skeleton_edit['height'] * abs(
+                (src_skeleton_edit['keypoints_body'][10][1] + src_skeleton_edit['keypoints_body'][13][1]) / 2 -
+                src_skeleton_edit['keypoints_body'][0][1])
+            dst_height_edit = dst_skeleton_edit['height'] * abs(
+                (dst_skeleton_edit['keypoints_body'][10][1] + dst_skeleton_edit['keypoints_body'][13][1]) / 2 -
+                dst_skeleton_edit['keypoints_body'][0][1])
+            scale_min_edit = 1.0 * src_height_edit / dst_height_edit
+        elif src_skeleton_edit['keypoints_body'][0] != None and src_skeleton_edit['keypoints_body'][8] != None and src_skeleton_edit['keypoints_body'][11] != None and \
+            dst_skeleton_edit['keypoints_body'][0] != None and dst_skeleton_edit['keypoints_body'][8] != None and dst_skeleton_edit['keypoints_body'][11] != None and \
+                src_skeleton_edit['keypoints_body'][0][2] > 0.5 and src_skeleton_edit['keypoints_body'][8][2] > 0.5 and src_skeleton_edit['keypoints_body'][11][2] > 0.5 and \
+            dst_skeleton_edit['keypoints_body'][0][2] > 0.5 and dst_skeleton_edit['keypoints_body'][8][2] > 0.5 and dst_skeleton_edit['keypoints_body'][11][2] > 0.5:
+
+            src_height_edit = src_skeleton_edit['height'] * abs(
+                (src_skeleton_edit['keypoints_body'][8][1] + src_skeleton_edit['keypoints_body'][11][1]) / 2 -
+                src_skeleton_edit['keypoints_body'][0][1])
+            dst_height_edit = dst_skeleton_edit['height'] * abs(
+                (dst_skeleton_edit['keypoints_body'][8][1] + dst_skeleton_edit['keypoints_body'][11][1]) / 2 -
+                dst_skeleton_edit['keypoints_body'][0][1])
+            scale_min_edit = 1.0 * src_height_edit / dst_height_edit
+        else:
+            scale_min_edit = np.sqrt(src_skeleton_edit['height'] * src_skeleton_edit['width']) / np.sqrt(dst_skeleton_edit['height'] * dst_skeleton_edit['width'])
+            scale_ratio_flag = True
+        
+        # Flux may change the scale, compensate for it here
+        ratio_src = calculate_scale_ratio(src_skeleton, src_skeleton_edit, scale_ratio_flag)
+        ratio_dst = calculate_scale_ratio(dst_skeleton, dst_skeleton_edit, scale_ratio_flag)
+
+        dst_skeleton_edit['height'] = int(dst_skeleton_edit['height'] * scale_min_edit)
+        dst_skeleton_edit['width'] = int(dst_skeleton_edit['width'] * scale_min_edit)
+        for idx in range(len(dst_skeleton_edit['keypoints_left_hand'])):
+            dst_skeleton_edit['keypoints_left_hand'][idx][0] *= scale_min_edit
+            dst_skeleton_edit['keypoints_left_hand'][idx][1] *= scale_min_edit
+        for idx in range(len(dst_skeleton_edit['keypoints_right_hand'])):
+            dst_skeleton_edit['keypoints_right_hand'][idx][0] *= scale_min_edit
+            dst_skeleton_edit['keypoints_right_hand'][idx][1] *= scale_min_edit
+    
+
+    dst_skeleton['height'] = int(dst_skeleton['height'] * scale_min)
+    dst_skeleton['width'] = int(dst_skeleton['width'] * scale_min)
+    for idx in range(len(dst_skeleton['keypoints_left_hand'])):
+        dst_skeleton['keypoints_left_hand'][idx][0] *= scale_min
+        dst_skeleton['keypoints_left_hand'][idx][1] *= scale_min
+    for idx in range(len(dst_skeleton['keypoints_right_hand'])):
+        dst_skeleton['keypoints_right_hand'][idx][0] *= scale_min
+        dst_skeleton['keypoints_right_hand'][idx][1] *= scale_min
+
+
+    dst_body_flag = check_full_body(dst_skeleton['keypoints_body'], threshold)
+    src_body_flag = check_full_body(src_skeleton_ori['keypoints_body'], threshold)
+    body_flag = check_full_body_both(dst_body_flag, src_body_flag)
+    #print('body_flag: ', body_flag)
+
+    if use_edit_for_base:
+        src_skeleton_edit = fix_lack_keypoints_use_sym(src_skeleton_edit)
+        dst_skeleton_edit = fix_lack_keypoints_use_sym(dst_skeleton_edit)
+    else:
+        src_skeleton = fix_lack_keypoints_use_sym(src_skeleton)
+        dst_skeleton = fix_lack_keypoints_use_sym(dst_skeleton)
+
+    none_idx = []
+    for idx in range(len(dst_skeleton['keypoints_body'])):
+        if dst_skeleton['keypoints_body'][idx] == None or src_skeleton['keypoints_body'][idx] == None:
+            src_skeleton['keypoints_body'][idx] = None
+            dst_skeleton['keypoints_body'][idx] = None
+            none_idx.append(idx)
+
+    # get bone ratio list
+    ratio_list, src_length_list, dst_length_list = [], [], []
+    for idx, limb in enumerate(limbSeq):
+        if use_edit_for_base:
+            src_X, src_Y, src_length = get_length(src_skeleton_edit, limb)
+            dst_X, dst_Y, dst_length = get_length(dst_skeleton_edit, limb)
+
+            if src_X is None or src_Y is None or dst_X is None or dst_Y is None:
+                ratio = -1
+            else:
+                ratio = 1.0 * dst_length * ratio_dst / src_length / ratio_src
+        
+        else:
+            src_X, src_Y, src_length = get_length(src_skeleton, limb)
+            dst_X, dst_Y, dst_length = get_length(dst_skeleton, limb)
+
+            if src_X is None or src_Y is None or dst_X is None or dst_Y is None:
+                ratio = -1
+            else:
+                ratio = 1.0 * dst_length / src_length
+
+        ratio_list.append(ratio)
+        src_length_list.append(src_length)
+        dst_length_list.append(dst_length)
+    
+    for idx, ratio in enumerate(ratio_list):
+        if ratio == -1:
+            if ratio_list[0] != -1 and ratio_list[1] != -1:
+                ratio_list[idx] = (ratio_list[0] + ratio_list[1]) / 2
+
+    # Consider adding constraints when Flux fails to correct head pose, causing neck issues.
+    # if ratio_list[12] > (ratio_list[0]+ratio_list[1])/2*1.25:
+    #     ratio_list[12] = (ratio_list[0]+ratio_list[1])/2*1.25
+    
+    ratio_list, src_length_list, dst_length_list = rescale_shorten_skeleton(ratio_list, src_length_list, dst_length_list)
+
+    rescaled_src_skeleton_ori = rescale_skeleton(src_skeleton_ori['height'], src_skeleton_ori['width'],
+                                                 src_skeleton_ori['keypoints_body'], ratio_list)
+
+    # get global translation offset_x and offset_y
+    if body_flag == 'full_body':
+        #print('use foot mark.')
+        dst_ground_y = max(dst_skeleton['keypoints_body'][10][1], dst_skeleton['keypoints_body'][13][1]) * dst_skeleton[
+            'height']
+        # The midpoint between toe and ankle
+        if dst_skeleton['keypoints_body'][18] != None and dst_skeleton['keypoints_body'][19] != None:
+            right_foot_mid = (dst_skeleton['keypoints_body'][10][1] + dst_skeleton['keypoints_body'][19][1]) / 2
+            left_foot_mid = (dst_skeleton['keypoints_body'][13][1] + dst_skeleton['keypoints_body'][18][1]) / 2
+            dst_ground_y = max(left_foot_mid, right_foot_mid) * dst_skeleton['height']
+
+        rescaled_src_ground_y = max(rescaled_src_skeleton_ori[10][1], rescaled_src_skeleton_ori[13][1])
+        delta_ground_y = rescaled_src_ground_y - dst_ground_y
+       
+        dst_ground_x = (dst_skeleton['keypoints_body'][8][0] + dst_skeleton['keypoints_body'][11][0]) * dst_skeleton[
+            'width'] / 2
+        rescaled_src_ground_x = (rescaled_src_skeleton_ori[8][0] + rescaled_src_skeleton_ori[11][0]) / 2
+        delta_ground_x = rescaled_src_ground_x - dst_ground_x
+        delta_x, delta_y = delta_ground_x, delta_ground_y
+
+    else:
+        #print('use neck mark.')
+        # use neck keypoint as mark
+        src_neck_y = rescaled_src_skeleton_ori[1][1]
+        dst_neck_y = dst_skeleton['keypoints_body'][1][1]
+        delta_neck_y = src_neck_y - dst_neck_y * dst_skeleton['height']
+
+        src_neck_x = rescaled_src_skeleton_ori[1][0]
+        dst_neck_x = dst_skeleton['keypoints_body'][1][0]
+        delta_neck_x = src_neck_x - dst_neck_x * dst_skeleton['width']
+        delta_x, delta_y = delta_neck_x, delta_neck_y
+        rescaled_src_ground_x = src_neck_x
+
+
+    dst_shape = (dst_skeleton_ori_w, dst_skeleton_ori_h)
+    output = write_to_poses(all_src_skeleton, none_idx, dst_shape, ratio_list, delta_x, delta_y,
+                                rescaled_src_ground_x, body_flag, scale_min)
+    return output
+
+
+def get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, tql_edit_pose_meta0, refer_edit_pose_meta):
+
+    for key, value in tpl_pose_meta0.items():
+        if type(value) is np.ndarray:
+            if key in ['keypoints_left_hand', 'keypoints_right_hand']:
+                value = value * np.array([[tpl_pose_meta0["width"], tpl_pose_meta0["height"], 1.0]])
+            if not isinstance(value, list):
+                value = value.tolist()
+        tpl_pose_meta0[key] = value
+
+    for key, value in refer_pose_meta.items():
+        if type(value) is np.ndarray:
+            if key in ['keypoints_left_hand', 'keypoints_right_hand']:
+                value = value * np.array([[refer_pose_meta["width"], refer_pose_meta["height"], 1.0]])
+            if not isinstance(value, list):
+                value = value.tolist()
+        refer_pose_meta[key] = value
+
+    tpl_pose_metas_new = []
+    for meta in tpl_pose_metas:
+        for key, value in meta.items():
+            if type(value) is np.ndarray:
+                if key in ['keypoints_left_hand', 'keypoints_right_hand']:
+                    value = value * np.array([[meta["width"], meta["height"], 1.0]])
+                if not isinstance(value, list):
+                    value = value.tolist()
+            meta[key] = value
+        tpl_pose_metas_new.append(meta)
+
+    if tql_edit_pose_meta0 is not None:
+        for key, value in tql_edit_pose_meta0.items():
+            if type(value) is np.ndarray:
+                if key in ['keypoints_left_hand', 'keypoints_right_hand']:
+                    value = value * np.array([[tql_edit_pose_meta0["width"], tql_edit_pose_meta0["height"], 1.0]])
+                if not isinstance(value, list):
+                    value = value.tolist()
+            tql_edit_pose_meta0[key] = value
+    
+    if refer_edit_pose_meta is not None:
+        for key, value in refer_edit_pose_meta.items():
+            if type(value) is np.ndarray:
+                if key in ['keypoints_left_hand', 'keypoints_right_hand']:
+                    value = value * np.array([[refer_edit_pose_meta["width"], refer_edit_pose_meta["height"], 1.0]])
+                if not isinstance(value, list):
+                    value = value.tolist()
+            refer_edit_pose_meta[key] = value
+
+    retarget_tpl_pose_metas = retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas_new, tql_edit_pose_meta0, refer_edit_pose_meta)
+
+    pose_metas = []
+    for meta in retarget_tpl_pose_metas:
+        pose_meta = AAPoseMeta()
+        width, height = meta["width"], meta["height"]
+        pose_meta.width = width
+        pose_meta.height = height
+        pose_meta.kps_body = np.array(meta["keypoints_body"])[:, :2] * (width, height)
+        pose_meta.kps_body_p = np.array(meta["keypoints_body"])[:, 2]
+
+        kps_lhand = []
+        kps_lhand_p = []
+        for each_kps_lhand in meta["keypoints_left_hand"]:
+            if each_kps_lhand is not None:
+                kps_lhand.append([each_kps_lhand.x, each_kps_lhand.y])
+                kps_lhand_p.append(each_kps_lhand.score)
+            else:
+                kps_lhand.append([None, None])
+                kps_lhand_p.append(0.0)
+
+        pose_meta.kps_lhand = np.array(kps_lhand)
+        pose_meta.kps_lhand_p = np.array(kps_lhand_p)
+
+        kps_rhand = []
+        kps_rhand_p = []
+        for each_kps_rhand in meta["keypoints_right_hand"]:
+            if each_kps_rhand is not None:
+                kps_rhand.append([each_kps_rhand.x, each_kps_rhand.y])
+                kps_rhand_p.append(each_kps_rhand.score)
+            else:
+                kps_rhand.append([None, None])
+                kps_rhand_p.append(0.0)
+
+        pose_meta.kps_rhand = np.array(kps_rhand)
+        pose_meta.kps_rhand_p = np.array(kps_rhand_p)
+
+        pose_metas.append(pose_meta)
+
+    return pose_metas
+

wan/modules/animate/preprocess/sam_utils.py

+# Copyright (c) 2025. Your modifications here.
+# This file wraps and extends sam2.utils.misc for custom modifications.
+
+from sam2.utils import misc as sam2_misc
+from sam2.utils.misc import * 
+from PIL import Image
+import numpy as np
+import torch
+from tqdm import tqdm
+import os
+
+import logging
+
+import torch
+from hydra import compose
+from hydra.utils import instantiate
+from omegaconf import OmegaConf
+
+from sam2.utils.misc import AsyncVideoFrameLoader, _load_img_as_tensor
+from sam2.build_sam import _load_checkpoint
+
+
+def _load_img_v2_as_tensor(img, image_size):
+    img_pil = Image.fromarray(img.astype(np.uint8))
+    img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size)))
+    if img_np.dtype == np.uint8:  # np.uint8 is expected for JPEG images
+        img_np = img_np / 255.0
+    else:
+        raise RuntimeError(f"Unknown image dtype: {img_np.dtype}")
+    img = torch.from_numpy(img_np).permute(2, 0, 1)
+    video_width, video_height = img_pil.size  # the original video size
+    return img, video_height, video_width
+
+def load_video_frames(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.485, 0.456, 0.406),
+    img_std=(0.229, 0.224, 0.225),
+    async_loading_frames=False,
+    frame_names=None,
+):
+    """
+    Load the video frames from a directory of JPEG files ("<frame_index>.jpg" format).
+
+    The frames are resized to image_size x image_size and are loaded to GPU if
+    `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.
+
+    You can load a frame asynchronously by setting `async_loading_frames` to `True`.
+    """
+    if isinstance(video_path, str) and os.path.isdir(video_path):
+        jpg_folder = video_path
+    else:
+        raise NotImplementedError("Only JPEG frames are supported at this moment")
+    if frame_names is None:
+        frame_names = [
+            p
+            for p in os.listdir(jpg_folder)
+            if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG", ".png"]
+        ]
+        frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+
+    num_frames = len(frame_names)
+    if num_frames == 0:
+        raise RuntimeError(f"no images found in {jpg_folder}")
+    img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]
+    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+
+    if async_loading_frames:
+        lazy_images = AsyncVideoFrameLoader(
+            img_paths, image_size, offload_video_to_cpu, img_mean, img_std
+        )
+        return lazy_images, lazy_images.video_height, lazy_images.video_width
+
+    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)
+    for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")):
+        images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)
+    if not offload_video_to_cpu:
+        images = images.cuda()
+        img_mean = img_mean.cuda()
+        img_std = img_std.cuda()
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, video_height, video_width
+
+
+def load_video_frames_v2(
+    frames,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.485, 0.456, 0.406),
+    img_std=(0.229, 0.224, 0.225),
+    async_loading_frames=False,
+    frame_names=None,
+):
+    """
+    Load the video frames from a directory of JPEG files ("<frame_index>.jpg" format).
+
+    The frames are resized to image_size x image_size and are loaded to GPU if
+    `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.
+
+    You can load a frame asynchronously by setting `async_loading_frames` to `True`.
+    """
+    num_frames = len(frames)
+    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+
+    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)
+    for n, frame in enumerate(tqdm(frames, desc="video frame")):
+        images[n], video_height, video_width = _load_img_v2_as_tensor(frame, image_size)
+    if not offload_video_to_cpu:
+        images = images.cuda()
+        img_mean = img_mean.cuda()
+        img_std = img_std.cuda()
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, video_height, video_width
+
+def build_sam2_video_predictor(
+    config_file,
+    ckpt_path=None,
+    device="cuda",
+    mode="eval",
+    hydra_overrides_extra=[],
+    apply_postprocessing=True,
+):
+    hydra_overrides = [
+        "++model._target_=video_predictor.SAM2VideoPredictor",
+    ]
+    if apply_postprocessing:
+        hydra_overrides_extra = hydra_overrides_extra.copy()
+        hydra_overrides_extra += [
+            # dynamically fall back to multi-mask if the single mask is not stable
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
+            # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking
+            "++model.binarize_mask_from_pts_for_mem_enc=true",
+            # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)
+            "++model.fill_hole_area=8",
+        ]
+
+    hydra_overrides.extend(hydra_overrides_extra)
+    # Read config and init model
+    cfg = compose(config_name=config_file, overrides=hydra_overrides)
+    OmegaConf.resolve(cfg)
+    model = instantiate(cfg.model, _recursive_=True)
+    _load_checkpoint(model, ckpt_path)
+    model = model.to(device)
+    if mode == "eval":
+        model.eval()
+    return model
\ No newline at end of file

wan/modules/animate/preprocess/utils.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import os
+import cv2
+import math
+import random
+import numpy as np
+
+def get_mask_boxes(mask):
+    """
+
+    Args:
+        mask: [h, w]
+    Returns:
+
+    """
+    y_coords, x_coords = np.nonzero(mask)
+    x_min = x_coords.min()
+    x_max = x_coords.max()
+    y_min = y_coords.min()
+    y_max = y_coords.max()
+    bbox = np.array([x_min, y_min, x_max, y_max]).astype(np.int32)
+    return bbox
+
+
+def get_aug_mask(body_mask, w_len=10, h_len=20):
+    body_bbox = get_mask_boxes(body_mask)
+
+    bbox_wh = body_bbox[2:4] - body_bbox[0:2]
+    w_slice = np.int32(bbox_wh[0] / w_len)
+    h_slice = np.int32(bbox_wh[1] / h_len)
+
+    for each_w in range(body_bbox[0], body_bbox[2], w_slice):
+        w_start = min(each_w, body_bbox[2])
+        w_end = min((each_w + w_slice), body_bbox[2])
+        # print(w_start, w_end)
+        for each_h in range(body_bbox[1], body_bbox[3], h_slice):
+            h_start = min(each_h, body_bbox[3])
+            h_end = min((each_h + h_slice), body_bbox[3])
+            if body_mask[h_start:h_end, w_start:w_end].sum() > 0:
+                body_mask[h_start:h_end, w_start:w_end] = 1
+
+    return body_mask
+    
+def get_mask_body_img(img_copy, hand_mask, k=7, iterations=1):
+    kernel = np.ones((k, k), np.uint8)
+    dilation = cv2.dilate(hand_mask, kernel, iterations=iterations)
+    mask_hand_img = img_copy * (1 - dilation[:, :, None])
+
+    return mask_hand_img, dilation
+
+
+def get_face_bboxes(kp2ds, scale, image_shape, ratio_aug):
+    h, w = image_shape
+    kp2ds_face = kp2ds.copy()[23:91, :2]
+
+    min_x, min_y = np.min(kp2ds_face, axis=0)
+    max_x, max_y = np.max(kp2ds_face, axis=0)
+
+
+    initial_width = max_x - min_x
+    initial_height = max_y - min_y
+
+    initial_area = initial_width * initial_height
+
+    expanded_area = initial_area * scale
+
+    new_width = np.sqrt(expanded_area * (initial_width / initial_height))
+    new_height = np.sqrt(expanded_area * (initial_height / initial_width))
+
+    delta_width = (new_width - initial_width) / 2
+    delta_height = (new_height - initial_height) / 4
+
+    if ratio_aug:
+        if random.random() > 0.5:
+            delta_width += random.uniform(0, initial_width // 10)
+        else:
+            delta_height += random.uniform(0, initial_height // 10)
+
+    expanded_min_x = max(min_x - delta_width, 0)
+    expanded_max_x = min(max_x + delta_width, w)
+    expanded_min_y = max(min_y - 3 * delta_height, 0)
+    expanded_max_y = min(max_y + delta_height, h)
+
+    return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)]
+
+
+def calculate_new_size(orig_w, orig_h, target_area, divisor=64):
+
+    target_ratio = orig_w / orig_h
+
+    def check_valid(w, h):
+
+        if w <= 0 or h <= 0:
+            return False
+        return (w * h <= target_area and  
+                w % divisor == 0 and  
+                h % divisor == 0)  
+
+    def get_ratio_diff(w, h):
+
+        return abs(w / h - target_ratio)
+
+    def round_to_64(value, round_up=False, divisor=64):
+
+        if round_up:
+            return divisor * ((value + (divisor - 1)) // divisor)
+        return divisor * (value // divisor)
+
+    possible_sizes = []
+
+    max_area_h = int(np.sqrt(target_area / target_ratio))
+    max_area_w = int(max_area_h * target_ratio)
+
+    max_h = round_to_64(max_area_h, round_up=True, divisor=divisor)
+    max_w = round_to_64(max_area_w, round_up=True, divisor=divisor)
+
+    for h in range(divisor, max_h + divisor, divisor):
+        ideal_w = h * target_ratio
+
+        w_down = round_to_64(ideal_w)
+        w_up = round_to_64(ideal_w, round_up=True)
+
+        for w in [w_down, w_up]:
+            if check_valid(w, h, divisor):
+                possible_sizes.append((w, h, get_ratio_diff(w, h)))
+
+    if not possible_sizes:
+        raise ValueError("Can not find suitable size")
+
+    possible_sizes.sort(key=lambda x: (-x[0] * x[1], x[2]))
+
+    best_w, best_h, _ = possible_sizes[0]
+    return int(best_w), int(best_h)
+
+
+def resize_by_area(image, target_area, keep_aspect_ratio=True, divisor=64, padding_color=(0, 0, 0)):
+    h, w = image.shape[:2]
+    try:
+        new_w, new_h = calculate_new_size(w, h, target_area, divisor)
+    except:
+        aspect_ratio = w / h
+
+        if keep_aspect_ratio:
+            new_h = math.sqrt(target_area / aspect_ratio)
+            new_w = target_area / new_h
+        else:
+            new_w = new_h = math.sqrt(target_area)
+
+        new_w, new_h = int((new_w // divisor) * divisor), int((new_h // divisor) * divisor)
+
+    interpolation = cv2.INTER_AREA if (new_w * new_h < w * h) else cv2.INTER_LINEAR
+
+    resized_image = padding_resize(image, height=new_h, width=new_w, padding_color=padding_color,
+                                    interpolation=interpolation)
+    return resized_image
+
+
+def padding_resize(img_ori, height=512, width=512, padding_color=(0, 0, 0), interpolation=cv2.INTER_LINEAR):
+    ori_height = img_ori.shape[0]
+    ori_width = img_ori.shape[1]
+    channel = img_ori.shape[2]
+
+    img_pad = np.zeros((height, width, channel))
+    if channel == 1:
+        img_pad[:, :, 0] = padding_color[0]
+    else:
+        img_pad[:, :, 0] = padding_color[0]
+        img_pad[:, :, 1] = padding_color[1]
+        img_pad[:, :, 2] = padding_color[2]
+
+    if (ori_height / ori_width) > (height / width):
+        new_width = int(height / ori_height * ori_width)
+        img = cv2.resize(img_ori, (new_width, height), interpolation=interpolation)
+        padding = int((width - new_width) / 2)
+        if len(img.shape) == 2:
+            img = img[:, :, np.newaxis]  
+        img_pad[:, padding: padding + new_width, :] = img
+    else:
+        new_height = int(width / ori_width * ori_height)
+        img = cv2.resize(img_ori, (width, new_height), interpolation=interpolation)
+        padding = int((height - new_height) / 2)
+        if len(img.shape) == 2:
+            img = img[:, :, np.newaxis]  
+        img_pad[padding: padding + new_height, :, :] = img
+
+    img_pad = np.uint8(img_pad)
+
+    return img_pad
+
+
+def get_frame_indices(frame_num, video_fps, clip_length, train_fps):
+
+    start_frame = 0
+    times = np.arange(0, clip_length) / train_fps
+    frame_indices = start_frame + np.round(times * video_fps).astype(int)
+    frame_indices = np.clip(frame_indices, 0, frame_num - 1)
+
+    return frame_indices.tolist()
+
+
+def get_face_bboxes(kp2ds, scale, image_shape):
+    h, w = image_shape
+    kp2ds_face = kp2ds.copy()[1:] * (w, h)
+
+    min_x, min_y = np.min(kp2ds_face, axis=0)
+    max_x, max_y = np.max(kp2ds_face, axis=0)
+
+    initial_width = max_x - min_x
+    initial_height = max_y - min_y
+
+    initial_area = initial_width * initial_height
+
+    expanded_area = initial_area * scale
+
+    new_width = np.sqrt(expanded_area * (initial_width / initial_height))
+    new_height = np.sqrt(expanded_area * (initial_height / initial_width))
+
+    delta_width = (new_width - initial_width) / 2
+    delta_height = (new_height - initial_height) / 4
+
+    expanded_min_x = max(min_x - delta_width, 0)
+    expanded_max_x = min(max_x + delta_width, w)
+    expanded_min_y = max(min_y - 3 * delta_height, 0)
+    expanded_max_y = min(max_y + delta_height, h)
+
+    return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)]
\ No newline at end of file