tencent / Hunyuan3D-Omni

Hunyuan3D-Omni

Hunyuan3D-Omni is a unified framework for the controllable generation of 3D assets, which inherits the structure of Hunyuan3D 2.1. In contrast, Hunyuan3D-Omni constructs a unified control encoder to introduce additional control signals, including point cloud, voxel, skeleton, and bounding box.

Multi-Modal Conditional Control

• Bounding Box Control : Generate 3D models constrained by 3D bounding boxes
• Pose Control : Create 3D human models with specific skeletal poses
• Point Cloud Control : Generate 3D models guided by input point clouds
• Voxel Control : Create 3D models from voxel representations

🎁 Models Zoo

It takes 10 GB VRAM for generation.

Installation

Requirements

We test our model with Python 3.10.

pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt

Usage

Inference

Multi-Modal Inference

python inference.py --control_type <control_type> [--use_ema] [--flashvdm]

The control_type parameter has four available options:

point : Use point control type for inference.
voxel : Use voxel control type for inference.
bbox : Use bounding box control type for inference.
pose : Use pose control type for inference.

The --use_ema flag enables the use of Exponential Moving Average (EMA) model for more stable inference.

The --flashvdm flag enables FlashVDM optimization for faster inference speed.

Please choose the appropriate control_type based on your requirements. For example, if you want to use the point control type, you can run:

python inference.py --control_type point 
python inference.py --control_type point --use_ema
python inference.py --control_type point --flashvdm

Acknowledgements

We would like to thank the contributors to the TripoSG , Trellis , DINOv2 , Stable Diffusion , FLUX , diffusers , HuggingFace , CraftsMan3D , Michelangelo , Hunyuan-DiT , HunyuanVideo , HunyuanWorld-1.0 , and HunyuanWorld-Voyager repositories, for their open research and exploration.

Citation

If you use this code in your research, please cite:

@misc{hunyuan3d2025hunyuan3domni,
      title={Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets}, 
      author={Tencent Hunyuan3D Team},
      year={2025},
      eprint={2509.21245},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.21245}, 
}
@misc{hunyuan3d2025hunyuan3d,
    title={Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material},
    author={Tencent Hunyuan3D Team},
    year={2025},
    eprint={2506.15442},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@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{yang2024hunyuan3d,
    title={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}
}

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