hunyuan3d-2

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“ Living out everyone’s imagination on creating and manipulating 3D assets.”

🔥 News

• Jan 21, 2025: 💬 Enjoy exciting 3D generation on our website Hunyuan3D Studio!
• Jan 21, 2025: 💬 Release inference code and pretrained models

• Jan 21, 2025: 💬 Release Hunyuan3D 2.0. Please give it a try

Abstract

We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale texture synthesis model - Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and e.t.c.

☯️ 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.

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:

Pretrained Models

| Model | Date | Huggingface | |----------------------|------------|--------------------------------------------------------| | Hunyuan3D-DiT-v2-0 | 2025-01-21 | Download | | Hunyuan3D-Paint-v2-0 | 2025-01-21 | Download |

🤗 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 site. Then install the other requirements via

pip install -r requirements.txt
<h1 class="text-2xl font-bold mt-8 mb-4 text-gray-900 dark:text-white">for texture</h1>
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:

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, which you could save to glb/obj (or other format) file.

For Hunyuan3D-Paint, do the following:

from hy3dgen.texgen import Hunyuan3DPaintPipeline
from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
<h1 class="text-2xl font-bold mt-8 mb-4 text-gray-900 dark:text-white">let's generate a mesh first</h1>
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 for more advanced usage, such as text to 3D and **texture generation for handcrafted mesh**.

Gradio App

You could also host a Gradio App in your own computer via:

python3 gradio_app.py

Don't forget to visit Hunyuan3D 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:

@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, Stable Diffusion, FLUX, diffusers, HuggingFace, CraftsMan3D, and Michelangelo repositories, for their open research and exploration.

Find Us

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