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Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

Paper · 2025 · Zibo Zhao, Zeqiang Lai, Qingxiang Lin et al.

Summary

Making 3D assets for games, film, simulation and embodied AI is slow and expert-driven. Unlike images, language or video, the 3D field has had no strong open foundation model to build on. Hunyuan3D 2.0 fills this gap with an image-conditioned, two-stage system that first builds an untextured mesh and then paints it.

For geometry, Hunyuan3D-ShapeVAE turns surface point clouds (coordinates and normals) into latent tokens. Its decoder predicts an SDF, which marching cubes converts into a mesh. Extra samples taken along edges and corners help preserve sharp detail. Hunyuan3D-DiT, a flow-matching diffusion transformer with FLUX-style dual- and single-stream blocks, generates these tokens from an input picture.

Hunyuan3D-Paint renders normal and position maps of the mesh. It then produces consistent multi-view images that follow the reference closely and bakes them into high-resolution texture maps. It works on generated meshes and on hand-modelled ones.

Compared with open and commercial competitors, the system shows better image alignment, finer detail and stronger user preference. Code and weights are public.

Key points

  • The system has four parts: Hunyuan3D-ShapeVAE, Hunyuan3D-DiT, Hunyuan3D-Paint and the Hunyuan3D-Studio platform. Studio supports low-poly conversion, sketch-to-3D and animation.
  • ShapeVAE combines uniform and importance-sampled points, applying FPS to each set separately. It is trained with an SDF MSE loss plus a KL term and uses randomly varied token lengths. The released model supports up to 3072 tokens.
  • Paint adds image delighting, a multi-task attention module for view consistency and reference fidelity, and super-resolution before baking.
  • The authors compared it against Trellis, three closed-source commercial products and separate shape and texture models. They also ran a user study with 300 test cases and 50 participants.
  • The code and weights are released at github.com/Tencent/Hunyuan3D-2.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 worksEngineering · 133 worksComputer Graphics and Computer-Aided Design · 7 worksComputational Mechanics · 1 work

Cite

Cite as Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, Huiwen Shi, Sicong Liu, Junta Wu, Yihang Lian, Fan Yang, Ruining Tang, Zebin He, Xinzhou Wang, Jian Liu, Xuhui Zuo, Zhuo Chen, Biwen Lei, Haohan Weng, Jing Xu, Yiling Zhu, Xinhai Liu, Lixin Xu, Changrong Hu, Shaoxiong Yang, Song Zhang, Yang Liu, Tianyu Huang, Lifu Wang, Jihong Zhang, Meng Chen, Liang Dong, Yiwen Jia, Yulin Cai, Jiaao Yu, Yixuan Tang, Hao Zhang, Zheng Ye, Peng He, Runzhou Wu, Chao Zhang, Yonghao Tan, Jie Xiao, Yangyu Tao, Jianchen Zhu, Jinbao Xue, Kai Liu, Chongqing Zhao, Xinming Wu, Zhichao Hu, Lei Qin, Jianbing Peng, Zhan Li, Minghui Chen, Xipeng Zhang, Lin Niu, Paige Wang, Yingkai Wang, Haozhao Kuang, Zhongyi Fan, Xu Zheng, Weihao Zhuang, YingPing He, Tian Liu, Yong Yang, Di Wang, Yuhong Liu, Jie Jiang, Jingwei Huang and Chunchao Guo (refer to the report for detailed contributions). 2025. Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation. arXiv:2501.12202.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2501.12202 · arXiv 2501.12202

@misc{zhao2025hunyuan3d,
  title = {{Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}},
  author = {Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, Huiwen Shi, Sicong Liu, Junta Wu, Yihang Lian, Fan Yang, Ruining Tang, Zebin He, Xinzhou Wang, Jian Liu, Xuhui Zuo, Zhuo Chen, Biwen Lei, Haohan Weng, Jing Xu, Yiling Zhu, Xinhai Liu, Lixin Xu, Changrong Hu, Shaoxiong Yang, Song Zhang, Yang Liu, Tianyu Huang, Lifu Wang, Jihong Zhang, Meng Chen, Liang Dong, Yiwen Jia, Yulin Cai, Jiaao Yu, Yixuan Tang, Hao Zhang, Zheng Ye, Peng He, Runzhou Wu, Chao Zhang, Yonghao Tan, Jie Xiao, Yangyu Tao, Jianchen Zhu, Jinbao Xue, Kai Liu, Chongqing Zhao, Xinming Wu, Zhichao Hu, Lei Qin, Jianbing Peng, Zhan Li, Minghui Chen, Xipeng Zhang, Lin Niu, Paige Wang, Yingkai Wang, Haozhao Kuang, Zhongyi Fan, Xu Zheng, Weihao Zhuang, YingPing He, Tian Liu, Yong Yang, Di Wang, Yuhong Liu, Jie Jiang, Jingwei Huang and Chunchao Guo (refer to the report for detailed contributions)},
  year = {2025},
  doi = {10.48550/arxiv.2501.12202},
  eprint = {2501.12202},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.12202}
}

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