🔥 News

• Jun 19, 2025: 👋 We present the technical report of Hunyuan3D-2.1, please check out the details and spark some discussion!
• Jun 13, 2025: 🤗 We release the first production-ready 3D asset generation model, Hunyuan3D-2.1!

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☯️ Hunyuan3D 2.1

Architecture

Tencent Hunyuan3D-2.1 is a scalable 3D asset creation system that advances state-of-the-art 3D generation through two pivotal innovations: Fully Open-Source Framework and Physically-Based Rendering (PBR) Texture Synthesis. For the first time, the system releases full model weights and training code, enabling community developers to directly fine-tune and extend the model for diverse downstream applications. This transparency accelerates academic research and industrial deployment. Moreover, replacing the prior RGB-based texture model, the upgraded PBR pipeline leverages physics-grounded material simulation to generate textures with photorealistic light interaction (e.g., metallic reflections, subsurface scattering).

Performance

We have evaluated Hunyuan3D 2.1 with other open-source as well as close-source 3d-generation methods. The numerical results indicate that Hunyuan3D 2.1 surpasses all baselines in the quality of generated textured 3D assets and the condition following ability.

Model	ULIP-T(⬆)	ULIP-I(⬆)	Uni3D-T(⬆)	Uni3D-I(⬆)
Michelangelo	0.0752	0.1152	0.2133	0.2611
Craftsman	0.0745	0.1296	0.2375	0.2987
TripoSG	0.0767	0.1225	0.2506	0.3129
Step1X-3D	0.0735	0.1183	0.2554	0.3195
Trellis	0.0769	0.1267	0.2496	0.3116
Direct3D-S2	0.0706	0.1134	0.2346	0.2930
Hunyuan3D-Shape-2.1	0.0774	0.1395	0.2556	0.3213

Model	CLIP-FiD(⬇)	CMMD(⬇)	CLIP-I(⬆)	LPIPS(⬇)
SyncMVD-IPA	28.39	2.397	0.8823	0.1423
TexGen	28.24	2.448	0.8818	0.1331
Hunyuan3D-2.0	26.44	2.318	0.8893	0.1261
Hunyuan3D-Paint-2.1	24.78	2.191	0.9207	0.1211

🎁 Models Zoo

It takes 10 GB VRAM for shape generation, 21GB for texture generation and 29GB for shape and texture generation in total.

Model	Description	Date	Size	Huggingface
Hunyuan3D-Shape-v2-1	Image to Shape Model	2025-06-14	3.3B	Download
Hunyuan3D-Paint-v2-1	Texture Generation Model	2025-06-14	2B	Download

🤗 Get Started with Hunyuan3D 2.1

Hunyuan3D 2.1 supports Macos, Windows, Linux. You may follow the next steps to use Hunyuan3D 2.1 via:

Install Requirements

We test our model on an A100 GPU with Python 3.10 and PyTorch 2.5.1+cu124.

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

cd hy3dpaint/custom_rasterizer
pip install -e .
cd ../..
cd hy3dpaint/DifferentiableRenderer
bash compile_mesh_painter.sh
cd ../..

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P hy3dpaint/ckpt

Code Usage

We designed a diffusers-like API to use our shape generation model - Hunyuan3D-Shape and texture synthesis model - Hunyuan3D-Paint.

import sys
sys.path.insert(0, './hy3dshape')
sys.path.insert(0, './hy3dpaint')
from textureGenPipeline import Hunyuan3DPaintPipeline
from textureGenPipeline import Hunyuan3DPaintPipeline, Hunyuan3DPaintConfig
from hy3dshape.pipelines import Hunyuan3DDiTFlowMatchingPipeline

# let's generate a mesh first
shape_pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2.1')
mesh_untextured = shape_pipeline(image='assets/demo.png')[0]

paint_pipeline = Hunyuan3DPaintPipeline(Hunyuan3DPaintConfig(max_num_view=6, resolution=512))
mesh_textured = paint_pipeline(mesh_path, image_path='assets/demo.png')

Gradio App

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

python3 gradio_app.py \
  --model_path tencent/Hunyuan3D-2.1 \
  --subfolder hunyuan3d-dit-v2-1 \
  --texgen_model_path tencent/Hunyuan3D-2.1 \
  --low_vram_mode

🔗 BibTeX

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

@misc{hunyuan3d2025hunyuan3d,
    title={Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material},
    author={Team Hunyuan3D and Shuhui Yang and Mingxin Yang and Yifei Feng and Xin Huang and Sheng Zhang and Zebin He and Di Luo and Haolin Liu and Yunfei Zhao and Qingxiang Lin and Zeqiang Lai and Xianghui Yang and Huiwen Shi and Zibo Zhao and Bowen Zhang and Hongyu Yan and Lifu Wang and Sicong Liu and Jihong Zhang and Meng Chen and Liang Dong and Yiwen Jia and Yulin Cai and Jiaao Yu and Yixuan Tang and Dongyuan Guo and Junlin Yu and Hao Zhang and Zheng Ye and Peng He and Runzhou Wu and Shida Wei and Chao Zhang and Yonghao Tan and Yifu Sun and Lin Niu and Shirui Huang and Bojian Zheng and Shu Liu and Shilin Chen and Xiang Yuan and Xiaofeng Yang and Kai Liu and Jianchen Zhu and Peng Chen and Tian Liu and Di Wang and Yuhong Liu and Linus and Jie Jiang and Jingwei Huang and Chunchao Guo},
    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}
}

Acknowledgements

We would like to thank the contributors to the TripoSG, Trellis, DINOv2, Stable Diffusion, FLUX, diffusers, HuggingFace, CraftsMan3D, and Michelangelo repositories, for their open research and exploration.

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