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[CVPR 2025] Official implementation of "Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation"
| Date | Stars |
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| 2026-07-24 | 296 |
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# Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
<a href="https://ltt-o.github.io/Kiss3dgen.github.io"><img src="https://img.shields.io/badge/Project_Page-Online-EA3A97"></a>
<a href="https://arxiv.org/abs/2503.01370"><img src="https://img.shields.io/badge/ArXiv-2503.01370-brightgreen"></a>
<a href="https://huggingface.co/spaces/LTT/Kiss3DGen"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Gradio%20Demo-Huggingface-orange"></a>
<a href="https://envision-research.hkust-gz.edu.cn/kiss3dgen/" style="pointer-events: none;"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Gradio%20Demo%20(Local)-lightgrey"></a>
<a href="https://gen3d.funnycp.com/"><img src="https://img.shields.io/badge/%E5%95%86%E7%94%A8%E5%B9%B3%E5%8F%B0-%E8%B6%A3%E4%B8%B8%E4%B8%87%E7%9B%B8-blue"></a>
</div>
---
# 🚩 Features
- [✅] Release inference code.
- [✅] Release model weights.
- [✅] Release huggingface gradio demo. Please try it at [HF demo](https://huggingface.co/spaces/LTT/Kiss3DGen) link.
- [✅] Release local gradio demo (more applications). Please try it at [Local demo](https://envision-research.hkust-gz.edu.cn/kiss3dgen/) link.
# ⚙️ Dependencies and Installation
We recommend using `Python>=3.10`, `PyTorch>=2.4.0`, and `CUDA>=12.1`.
```bash
conda create --name kiss3dgen python=3.10
conda activate kiss3dgen
pip install -U pip
# Install the correct version of CUDA
conda install cuda -c nvidia/label/cuda-12.1.0
# Install PyTorch and xformers
# You may need to install another xformers version if you use a different PyTorch version
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121
pip install xformers==0.0.27.post1
# Install Pytorch3D
pip install iopath
pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt240/download.html
# Install torch-scatter
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.4.0+cu121.html
# Install other requirements
pip install -r requirements.txt
```
# 💫 Inference
## Download the pretrained model
Our inference script will download the models automatically. Alternatively, you can run the following command to download them manually, it will put them under the `checkpoint/` directory.
```bash
# Download the pretrained models
python ./download_models.py
```
## 3D Asset Generation
we run it on A800 GPU with 80GB memory, if you only have a smaller GPU, you can change the models' device in the `pipeline/pipeline_config/default.yaml` file to use two or more smaller memory GPUs.
```bash
# Text-to-3D
python ./pipeline/example_text_to_3d.py
# Image-to-3D
python ./pipeline/example_image_to_3d.py
# 3D-to-3D
python ./pipeline/example_3d_to_3d.py
```
## Gradio Demo
Interactive inference: Run your local gradio demo.
```bash
python ./app.py
```
# 📜 Citation
If you find our work useful for your research or applications, please cite using this BibTeX:
```BibTeX
@article{lin2025kiss3dgen,
title={Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation},
author={Lin, Jiantao and Yang, Xin and Chen, Meixi and Xu, Yingjie and Yan, Dongyu and Wu, Leyi and Xu, Xinli and Xu, Lie and Zhang, Shunsi and Chen, Ying-Cong},
journal={arXiv preprint arXiv:2503.01370},
year={2025}
}
```
# 🤗 Acknowledgements
We thank the authors of the following projects for their excellent contributions to 3D generative AI!
- [PRM](https://github.com/g3956/PRM)
- [FlexGen](https://xxu068.github.io/flexgen.github.io/)
- [Unique3D](https://github.com/AiuniAI/Unique3D)
- [FluxGym](https://github.com/cocktailpeanut/fluxgym)
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