README.md

---
title: PolyGenixAI
emoji: 🌍
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 4.44.0

app_file: gradio_app.py
pinned: false
short_description: Text-to-3D and Image-to-3D Generation
models:
- tencent/Hunyuan3D-2
---


[中文阅读](README_zh_cn.md)
[日本語で読む](README_ja_jp.md)

<p align="center"> 
  <img src="./assets/images/teaser.jpg">


</p>

<div align="center">
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> Join our **[Wechat](#find-us)** and **[Discord](#find-us)** group to discuss and find help from us.


<p align="center">
“ Living out everyone’s imagination on creating and manipulating 3D assets.”
</p>

## 🔥 News

- Jan 21, 2025: 💬 Enjoy exciting 3D generation on our website [Hunyuan3D Studio](https://3d.hunyuan.tencent.com)!
- Jan 21, 2025: 💬 Release inference code and pretrained models
  of [Hunyuan3D 2.0](https://huggingface.co/tencent/Hunyuan3D-2).
- Jan 21, 2025: 💬 Release Hunyuan3D 2.0. Please give it a try
  via [huggingface space](https://huggingface.co/spaces/tencent/Hunyuan3D-2)
  our [official site](https://3d.hunyuan.tencent.com)!

## **Abstract**

PolyGenixAI: Fast and High-Quality 3D Asset Generation
We present PolyGenixAI, an advanced system for rapidly generating high-resolution textured 3D assets. This system comprises two core components: 
a high-speed shape generation model, PolyGenixAI-DiT, and a robust texture synthesis model, PolyGenixAI-Paint.

PolyGenixAI-DiT, a scalable flow-based diffusion transformer, delivers precise geometry aligned with input images in seconds, 
enabling efficient creation of 3D models for diverse applications.
PolyGenixAI-Paint leverages strong geometric and diffusion priors to produce vibrant, high-resolution texture maps for both generated and user-provided meshes.

Additionally, PolyGenixAI Studio offers a user-friendly platform that simplifies 3D asset creation and manipulation. 
It empowers both professionals and enthusiasts to quickly generate, edit, and animate 3D models with ease.
PolyGenixAI outperforms state-of-the-art models, delivering superior geometry details, condition alignment, and texture quality. 
Optimized for speed, it ensures fast model generation without compromising quality, making it ideal for real-time and production workflows.



<p align="center">
  <img src="assets/images/system.jpg">
</p>

## ☯️ **Hunyuan3D 2.0**

### Architecture

Hunyuan3D 2.0 features a two-stage generation pipeline, starting with the creation of a bare mesh, followed by the
synthesis of a texture map for that mesh. This strategy is effective for decoupling the difficulties of shape and
texture generation and also provides flexibility for texturing either generated or handcrafted meshes.

<p align="left">
  <img src="assets/images/arch.jpg">
</p>

### Performance

We have evaluated Hunyuan3D 2.0 with other open-source as well as close-source 3d-generation methods.
The numerical results indicate that Hunyuan3D 2.0 surpasses all baselines in the quality of generated textured 3D assets
and the condition following ability.

| Model                   | CMMD(⬇)   | FID_CLIP(⬇) | FID(⬇)      | CLIP-score(⬆) |
|-------------------------|-----------|-------------|-------------|---------------|
| Top Open-source Model1  | 3.591     | 54.639      | 289.287     | 0.787         |
| Top Close-source Model1 | 3.600     | 55.866      | 305.922     | 0.779         |
| Top Close-source Model2 | 3.368     | 49.744      | 294.628     | 0.806         |
| Top Close-source Model3 | 3.218     | 51.574      | 295.691     | 0.799         |
| Hunyuan3D 2.0           | **3.193** | **49.165**  | **282.429** | **0.809**     |

Generation results of Hunyuan3D 2.0:
<p align="left">
  <img src="assets/images/e2e-1.gif"  height=250>
  <img src="assets/images/e2e-2.gif"  height=250>
</p>

### Pretrained Models

| Model                | Date       | Huggingface                                            |
|----------------------|------------|--------------------------------------------------------| 
| Hunyuan3D-DiT-v2-0   | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |
| Hunyuan3D-Paint-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |

## 🤗 Get Started with Hunyuan3D 2.0

You may follow the next steps to use Hunyuan3D 2.0 via code or the Gradio App.

### Install Requirements

Please install Pytorch via the [official](https://pytorch.org/) site. Then install the other requirements via

```bash
pip install -r requirements.txt
# for texture
cd hy3dgen/texgen/custom_rasterizer
python3 setup.py install
cd hy3dgen/texgen/differentiable_renderer
bash compile_mesh_painter.sh
```

### API Usage

We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model -
Hunyuan3D-Paint.

You could assess **Hunyuan3D-DiT** via:

```python
from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline

pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
mesh = pipeline(image='assets/demo.png')[0]
```

The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other
format) file.

For **Hunyuan3D-Paint**, do the following:

```python
from hy3dgen.texgen import Hunyuan3DPaintPipeline
from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline

# let's generate a mesh first
pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
mesh = pipeline(image='assets/demo.png')[0]

pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
mesh = pipeline(mesh, image='assets/demo.png')
```

Please visit [minimal_demo.py](minimal_demo.py) for more advanced usage, such as **text to 3D** and **texture generation
for handcrafted mesh**.

### Gradio App

You could also host a [Gradio](https://www.gradio.app/) App in your own computer via:

```bash
python3 gradio_app.py
```

Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself.

## 📑 Open-Source Plan

- [x] Inference Code
- [x] Model Checkpoints
- [x] Technical Report
- [ ] ComfyUI
- [ ] TensorRT Version

## 🔗 BibTeX

If you found this repository helpful, please cite our reports:

```bibtex
@misc{hunyuan3d22025tencent,
    title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
    author={Tencent Hunyuan3D Team},
    year={2025},
}

@misc{yang2024tencent,
    title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
    year={2024},
    author={Tencent Hunyuan3D Team},
    eprint={2411.02293},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

## Acknowledgements

We would like to thank the contributors to
the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers), [HuggingFace](https://huggingface.co), [CraftsMan3D](https://github.com/wyysf-98/CraftsMan3D),
and [Michelangelo](https://github.com/NeuralCarver/Michelangelo/tree/main) repositories, for their open research and
exploration.

## Find Us

| Wechat Group | Xiaohongshu | X | Discord |
|--------------|-------------|---|---------|
|              |             |   |         |        

## Star History

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