Tencent/Hunyuan3D-2: High-Resolution 3D Assets Generation with Large Scale Hunyuan3D Diffusion Models.

中文阅读

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

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 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.

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.

Generation results of Hunyuan3D 2.0:

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

Please install Pytorch via the official site. Then install the other requirements via

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

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

# 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 for more advanced usage, such as text to 3D and texture generation
for handcrafted mesh.

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

Don’t forget to visit Hunyuan3D for quick use, if you don’t want to host yourself.

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}
}

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.

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