README.md · tencent/Hunyuan3D-2mv at main

metadata

library_name: hunyuan3d-2
license: other
license_name: tencent-hunyuan-community
license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt
language:
  - en
  - zh
tags:
  - image-to-3d
  - text-to-3d
pipeline_tag: image-to-3d

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

This repository contains the models of the paper Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation.

Hunyuan3D-2mv is finetuned from Hunyuan3D-2 to support multiview controlled shape generation.

🤗 Get Started with Hunyuan3D 2mv

Here is a simple usage:

from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
    'tencent/Hunyuan3D-2mv',
    subfolder='hunyuan3d-dit-v2-mv',
    use_safetensors=True,
    device='cuda'
)
mesh = pipeline(
    image={
        "front": "your front view image.png",
        "left": "your left view image.png",
        "back": "your back view image.png"
    },
    num_inference_steps=30,
    octree_resolution=380,
    num_chunks=20000,
    generator=torch.manual_seed(12345),
    output_type='trimesh'
)[0]

For code and more details on how to use it, refer to the Github repository.

🔗 BibTeX

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

@misc{hunyuan3d22025tencent,
    title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
    author={Tencent Hunyuan3D Team},
    year={2025},
    eprint={2501.12202},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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

Community Resources

Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0:

Acknowledgements

We would like to thank the contributors to the DINOv2, Stable Diffusion, FLUX, diffusers and HuggingFace repositories, for their open research and exploration.