v1.0

DreamTechAI/Direct3D/README.md

+---
+license: apache-2.0
+---
+
+# Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer (NeurIPS 2024)
+
+
+## ✨ News
+
+- Feb 11, 2025: 🔨 We are working on the Gradio demo and will release it soon!
+- Feb 11, 2025: 🎁 Enjoy our improved version of Direct3D with high quality geometry and texture at [https://www.neural4d.com](https://www.neural4d.com/).
+- Feb 11, 2025: 🚀 Release inference code of Direct3D and the pretrained models are available at 🤗 [Hugging Face](https://huggingface.co/DreamTechAI/Direct3D/tree/main).
+
+## 📝 Abstract
+
+We introduce **Direct3D**, a native 3D generative model scalable to in-the-wild input images, without requiring a multiview diffusion model or SDS optimization. Our approach comprises two primary components: a Direct 3D Variational Auto-Encoder **(D3D-VAE)** and a Direct 3D Diffusion Transformer **(D3D-DiT)**. D3D-VAE efficiently encodes high-resolution 3D shapes into a compact and continuous latent triplane space. Notably, our method directly supervises the decoded geometry using a semi-continuous surface sampling strategy, diverging from previous methods relying on rendered images as supervision signals. D3D-DiT models the distribution of encoded 3D latents and is specifically designed to fuse positional information from the three feature maps of the triplane latent, enabling a native 3D generative model scalable to large-scale 3D datasets. Additionally, we introduce an innovative image-to-3D generation pipeline incorporating semantic and pixel-level image conditions, allowing the model to produce 3D shapes consistent with the provided conditional image input. Extensive experiments demonstrate the superiority of our large-scale pre-trained Direct3D over previous image-to-3D approaches, achieving significantly better generation quality and generalization ability, thus establishing a new state-of-the-art for 3D content creation.
+
+
+## 🚀 Getting Started
+
+### Installation
+
+```sh
+git clone https://github.com/DreamTechAI/Direct3D.git
+
+cd Direct3D
+
+pip install -r requirements.txt
+
+pip install -e .
+```
+
+### Usage
+
+```python
+from direct3d.pipeline import Direct3dPipeline
+pipeline = Direct3dPipeline.from_pretrained("DreamTechAI/Direct3D")
+pipeline.to("cuda")
+mesh = pipeline(
+    "assets/devil.png",
+    remove_background=False, # set to True if the background of the image needs to be removed
+    mc_threshold=-1.0,
+    guidance_scale=4.0,
+    num_inference_steps=50,
+)["meshes"][0]
+mesh.export("output.obj")
+```
+
+## 🤗 Acknowledgements
+
+Thanks to the following repos for their great work, which helps us a lot in the development of Direct3D:
+
+- [3DShape2VecSet](https://github.com/1zb/3DShape2VecSet/tree/master)
+- [Michelangelo](https://github.com/NeuralCarver/Michelangelo)
+- [Objaverse](https://objaverse.allenai.org/)
+- [diffusers](https://github.com/huggingface/diffusers)
+
+## 📖 Citation
+
+If you find our work useful, please consider citing our paper:
+
+```bibtex
+@article{direct3d,
+  title={Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer},
+  author={Wu, Shuang and Lin, Youtian and Zhang, Feihu and Zeng, Yifei and Xu, Jingxi and Torr, Philip and Cao, Xun and Yao, Yao},
+  journal={arXiv preprint arXiv:2405.14832},
+  year={2024}
+}
+```
+
+---
\ No newline at end of file

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README.md

+# Direct3D
+Neural4D 2.0将3D AI的生成效果提升至“人工级水平”，算力需求暴降80%，DreamTech已开源其前期研究成果Neural4D 1.0（本项目）。
+
+## 论文
+`Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer`
+- https://arxiv.org/pdf/2405.14832
+
+## 模型结构
+Neural4D 1.0包含两个关键组件，D3D-VAE用于捕捉3D形状的关键特征，然后D3D-DiT利用像素级和语义级对齐模块，整合图像信息到扩散过程中。
+<div align=center>
+    <img src="./doc/Direct3D.png"/>
+</div>
+
+## 算法原理
+Neural4D 1.0采用3D生成的通用方案，先获取形状信息，再加入像素级细节信息，而暂未开源的Neural4D 2.0引入了全新的3D Assembly Generation思路，模拟真实世界的3D拓扑结构与人类设计师分部件三维建模的思路，并引入类似DeepSeek-R1的强化学习策略。
+<div align=center>
+    <img src="./doc/algorithm.png"/>
+</div>
+
+## 环境配置
+```
+mv Direct3D_pytorch Direct3D # 去框架名后缀
+```
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：b272aae8ec72
+docker run -it --shm-size=64G -v $PWD/Direct3D:/home/Direct3D -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name d3d <your IMAGE ID> bash
+cd /home/Direct3D
+pip install -r requirements.txt
+pip install -e .
+```
+### Dockerfile（方法二）
+```
+cd /home/Direct3D/docker
+docker build --no-cache -t d3d:latest .
+docker run --shm-size=64G --name d3d -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../Direct3D:/home/Direct3D -it d3d bash
+# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
+cd /home/Direct3D
+pip install -e .
+```
+### Anaconda（方法三）
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+- https://developer.hpccube.com/tool/
+```
+DTK驱动:dtk24.04.3
+python:python3.10
+torch:2.3.0
+torchvision:0.18.1
+torchaudio:2.1.2
+triton:2.1.0
+vllm:0.6.2
+flash-attn:2.6.1
+deepspeed:0.14.2
+apex:1.3.0
+xformers:0.0.25
+transformers:4.48.0
+```
+
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
+
+2、其它非特殊库参照requirements.txt安装
+```
+cd /home/Direct3D
+pip install -r requirements.txt
+pip install -e .
+```
+
+## 数据集
+`无`
+
+## 训练
+`无`
+
+## 推理
+### 单机单卡
+预训练权重目录结构：
+```
+/home/Direct3D/
+    ├── DreamTechAI/Direct3D
+    ├── openai/clip-vit-large-patch14
+    └── facebook/dinov2-large
+```
+
+```
+python test.py
+```
+
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+
+## result
+
+`输入: `
+```
+图片: assets/devil.png
+```
+`输出:`
+```
+3D结构: output.obj
+```
+在线3D模型结构查看工具：[lzz3Dview](https://stl.neurosurgery.icu/) 
+
+源作者提供的效果示例：
+
+<div align=center>
+  <img src="assets/demo/video2.gif", width="48%">
+  <img src="assets/demo/video1.gif", width="48%">
+  <br>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：pytorch。
+
+## 应用场景
+### 算法类别
+`3D生成`
+### 热点应用行业
+`动漫,广媒,影视,制造,医疗,家居,教育`
+## 预训练权重
+预训练权重快速下载中心：[SCNet AIModels](http://113.200.138.88:18080/aimodels) ，项目中的预训练权重可从快速下载通道下载：[Direct3D](http://113.200.138.88:18080/aimodels/dreamtechai/Direct3D.git)、[clip-vit-large-patch14](http://113.200.138.88:18080/aimodels/clip-vit-large-patch14.git)、[dinov2-large](http://113.200.138.88:18080/aimodels/facebook/dinov2-large.git)
+
+Hugging Face下载地址为：[Direct3D](https://huggingface.co/DreamTechAI/Direct3D)、[clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)、[dinov2-large](https://huggingface.co/facebook/dinov2-large)
+## 源码仓库及问题反馈
+- http://developer.sourcefind.cn/codes/modelzoo/Direct3D_pytorch.git
+## 参考资料
+- https://github.com/DreamTechAI/Direct3D.git
+

README_origin.md

+
+# Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer (NeurIPS 2024)
+
+<div align="center">
+  <a href=https://nju-3dv.github.io/projects/Direct3D/ target="_blank"><img src=https://img.shields.io/badge/Project%20Page-333399.svg?logo=googlehome height=22px></a>
+  <a href= target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
+  <a href=https://huggingface.co/DreamTechAI/Direct3D/tree/main target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
+  <a href=https://openreview.net/pdf?id=vCOgjBIZuL target="_blank"><img src=https://img.shields.io/badge/Paper-b5212f.svg?logo=paperswithcode height=22px></a>
+  <a href=https://arxiv.org/abs/2405.14832 target="_blank"><img src=https://img.shields.io/badge/Arxiv-b5212f.svg?logo=arxiv height=22px></a>
+</div>
+
+<p align="center">
+  <img src="assets/demo/video2.gif", width="48%">
+  <img src="assets/demo/video1.gif", width="48%">
+  <br>
+</p>
+
+---
+
+## ✨ News
+
+- Feb 11, 2025: 🔨 We are working on the Gradio demo and will release it soon!
+- Feb 11, 2025: 🎁 Enjoy our improved version of Direct3D with high quality geometry and texture at [https://www.neural4d.com](https://www.neural4d.com/).
+- Feb 11, 2025: 🚀 Release inference code of Direct3D and the pretrained models are available at 🤗 [Hugging Face](https://huggingface.co/DreamTechAI/Direct3D/tree/main).
+
+## 📝 Abstract
+
+We introduce **Direct3D**, a native 3D generative model scalable to in-the-wild input images, without requiring a multiview diffusion model or SDS optimization. Our approach comprises two primary components: a Direct 3D Variational Auto-Encoder **(D3D-VAE)** and a Direct 3D Diffusion Transformer **(D3D-DiT)**. D3D-VAE efficiently encodes high-resolution 3D shapes into a compact and continuous latent triplane space. Notably, our method directly supervises the decoded geometry using a semi-continuous surface sampling strategy, diverging from previous methods relying on rendered images as supervision signals. D3D-DiT models the distribution of encoded 3D latents and is specifically designed to fuse positional information from the three feature maps of the triplane latent, enabling a native 3D generative model scalable to large-scale 3D datasets. Additionally, we introduce an innovative image-to-3D generation pipeline incorporating semantic and pixel-level image conditions, allowing the model to produce 3D shapes consistent with the provided conditional image input. Extensive experiments demonstrate the superiority of our large-scale pre-trained Direct3D over previous image-to-3D approaches, achieving significantly better generation quality and generalization ability, thus establishing a new state-of-the-art for 3D content creation.
+
+<p align="center">
+  <img src="assets/figure/teaser.gif", width="99%">
+  <br>
+</p>
+
+## 🚀 Getting Started
+
+### Installation
+
+```sh
+git clone https://github.com/DreamTechAI/Direct3D.git
+
+cd Direct3D
+
+pip install -r requirements.txt
+
+pip install -e .
+```
+
+### Usage
+
+```python
+from direct3d.pipeline import Direct3dPipeline
+pipeline = Direct3dPipeline.from_pretrained("DreamTechAI/Direct3D")
+pipeline.to("cuda")
+mesh = pipeline(
+    "assets/devil.png",
+    remove_background=False, # set to True if the background of the image needs to be removed
+    mc_threshold=-1.0,
+    guidance_scale=4.0,
+    num_inference_steps=50,
+)["meshes"][0]
+mesh.export("output.obj")
+```
+
+## 🤗 Acknowledgements
+
+Thanks to the following repos for their great work, which helps us a lot in the development of Direct3D:
+
+- [3DShape2VecSet](https://github.com/1zb/3DShape2VecSet/tree/master)
+- [Michelangelo](https://github.com/NeuralCarver/Michelangelo)
+- [Objaverse](https://objaverse.allenai.org/)
+- [diffusers](https://github.com/huggingface/diffusers)
+
+## 📖 Citation
+
+If you find our work useful, please consider citing our paper:
+
+```bibtex
+@article{direct3d,
+  title={Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer},
+  author={Wu, Shuang and Lin, Youtian and Zhang, Feihu and Zeng, Yifei and Xu, Jingxi and Torr, Philip and Cao, Xun and Yao, Yao},
+  journal={arXiv preprint arXiv:2405.14832},
+  year={2024}
+}
+```
+
+---