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---
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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# 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
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# 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}
}
```
---
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