Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913903812804608 |
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| author | Lai, Zeqiang Zhao, Yunfei Liu, Haolin Zhao, Zibo Lin, Qingxiang Shi, Huiwen Yang, Xianghui Yang, Mingxin Yang, Shuhui Feng, Yifei Zhang, Sheng Huang, Xin Luo, Di Yang, Fan Yang, Fang Wang, Lifu Liu, Sicong Tang, Yixuan Cai, Yulin He, Zebin Liu, Tian Liu, Yuhong Jiang, Jie Linus Huang, Jingwei Guo, Chunchao |
| author_facet | Lai, Zeqiang Zhao, Yunfei Liu, Haolin Zhao, Zibo Lin, Qingxiang Shi, Huiwen Yang, Xianghui Yang, Mingxin Yang, Shuhui Feng, Yifei Zhang, Sheng Huang, Xin Luo, Di Yang, Fan Yang, Fang Wang, Lifu Liu, Sicong Tang, Yixuan Cai, Yulin He, Zebin Liu, Tian Liu, Yuhong Jiang, Jie Linus Huang, Jingwei Guo, Chunchao |
| contents | In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes. In terms of texture generation, it is upgraded with phyiscal-based rendering (PBR) via a novel multi-view architecture extended from Hunyuan3D 2.0 Paint model. Our extensive evaluation shows that Hunyuan3D 2.5 significantly outperforms previous methods in both shape and end-to-end texture generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details Lai, Zeqiang Zhao, Yunfei Liu, Haolin Zhao, Zibo Lin, Qingxiang Shi, Huiwen Yang, Xianghui Yang, Mingxin Yang, Shuhui Feng, Yifei Zhang, Sheng Huang, Xin Luo, Di Yang, Fan Yang, Fang Wang, Lifu Liu, Sicong Tang, Yixuan Cai, Yulin He, Zebin Liu, Tian Liu, Yuhong Jiang, Jie Linus Huang, Jingwei Guo, Chunchao Computer Vision and Pattern Recognition Artificial Intelligence In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes. In terms of texture generation, it is upgraded with phyiscal-based rendering (PBR) via a novel multi-view architecture extended from Hunyuan3D 2.0 Paint model. Our extensive evaluation shows that Hunyuan3D 2.5 significantly outperforms previous methods in both shape and end-to-end texture generation. |
| title | Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.16504 |