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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09231 |
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| _version_ | 1866908951925227520 |
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| author | He, Huiang Zhao, Shengchu Huang, Jianwen Li, Jie Wu, Jiaqi Zhang, Hu Tang, Pei Zheng, Heliang Li, Yukun Jia, Rongfei |
| author_facet | He, Huiang Zhao, Shengchu Huang, Jianwen Li, Jie Wu, Jiaqi Zhang, Hu Tang, Pei Zheng, Heliang Li, Yukun Jia, Rongfei |
| contents | Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these limitations, we propose Hitem3D 2.0, a multi-view guided native 3D texture generation framework that enhances texture quality through the integration of 2D multi-view generation priors and native 3D texture representations. Hitem3D 2.0 comprises two key components: a multi-view synthesis framework and a native 3D texture generation model. The multi-view generation is built upon a pre-trained image editing backbone and incorporates plug-and-play modules that explicitly promote geometric alignment, cross-view consistency, and illumination uniformity, thereby enabling the synthesis of high-fidelity multi-view images. Conditioned on the generated views and 3D geometry, the native 3D texture generation model projects multi-view textures onto 3D surfaces while plausibly completing textures in unseen regions. Through the integration of multi-view consistency constraints with native 3D texture modeling, Hitem3D 2.0 significantly improves texture completeness, cross-view coherence, and geometric alignment. Experimental results demonstrate that Hitem3D 2.0 outperforms existing methods in terms of texture detail, fidelity, consistency, coherence, and alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09231 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation He, Huiang Zhao, Shengchu Huang, Jianwen Li, Jie Wu, Jiaqi Zhang, Hu Tang, Pei Zheng, Heliang Li, Yukun Jia, Rongfei Computer Vision and Pattern Recognition Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these limitations, we propose Hitem3D 2.0, a multi-view guided native 3D texture generation framework that enhances texture quality through the integration of 2D multi-view generation priors and native 3D texture representations. Hitem3D 2.0 comprises two key components: a multi-view synthesis framework and a native 3D texture generation model. The multi-view generation is built upon a pre-trained image editing backbone and incorporates plug-and-play modules that explicitly promote geometric alignment, cross-view consistency, and illumination uniformity, thereby enabling the synthesis of high-fidelity multi-view images. Conditioned on the generated views and 3D geometry, the native 3D texture generation model projects multi-view textures onto 3D surfaces while plausibly completing textures in unseen regions. Through the integration of multi-view consistency constraints with native 3D texture modeling, Hitem3D 2.0 significantly improves texture completeness, cross-view coherence, and geometric alignment. Experimental results demonstrate that Hitem3D 2.0 outperforms existing methods in terms of texture detail, fidelity, consistency, coherence, and alignment. |
| title | Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.09231 |