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# Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets
This model was presented in the paper [Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets](https://huggingface.co/papers/2509.21245).
- **Project Page:** [https://3d.hunyuan.tencent.com](https://3d.hunyuan.tencent.com)
- **Code:** [https://github.com/Tencent-Hunyuan/Hunyuan3D-Omni](https://github.com/Tencent-Hunyuan/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.
| Model | Description | Date | Size | Huggingface |
|----------------------------|-----------------------------|------------|------|-------------------------------------------------------------------------------------------|
| Hunyuan3D-Omni | Image to Shape Model with multi-modal control | 2025-09-25 | 3.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-Omni/tree/main) |
## Installation
### Requirements
We test our model with Python 3.10.
```bash
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
```bash
python inference.py --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:
```bash
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](https://github.com/VAST-AI-Research/TripoSG), [Trellis](https://github.com/microsoft/TRELLIS), [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), [Michelangelo](https://github.com/NeuralCarver/Michelangelo/tree/main), [Hunyuan-DiT](https://github.com/Tencent-Hunyuan/HunyuanDiT), [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo), [HunyuanWorld-1.0](https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0), and [HunyuanWorld-Voyager](https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager) repositories, for their open research and
exploration.
## Citation
If you use this code in your research, please cite:
```bibtex
@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}
}
```
## Star History
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# Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets
This model was presented in the paper [Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets](https://huggingface.co/papers/2509.21245).
- **Project Page:** [https://3d.hunyuan.tencent.com](https://3d.hunyuan.tencent.com)
- **Code:** [https://github.com/Tencent-Hunyuan/Hunyuan3D-Omni](https://github.com/Tencent-Hunyuan/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.
