PEGAsus: 3D Personalization of Geometry and Appearance
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914315401953280 |
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| author | Hu, Jingyu Hu, Bin Hui, Ka-Hei Li, Haipeng Liu, Zhengzhe Cohen-Or, Daniel Fu, Chi-Wing |
| author_facet | Hu, Jingyu Hu, Bin Hui, Ka-Hei Li, Haipeng Liu, Zhengzhe Cohen-Or, Daniel Fu, Chi-Wing |
| contents | We present PEGAsus, a new framework capable of generating Personalized 3D shapes by learning shape concepts at both Geometry and Appearance levels. First, we formulate 3D shape personalization as extracting reusable, category-agnostic geometric and appearance attributes from reference shapes, and composing these attributes with text to generate novel shapes. Second, we design a progressive optimization strategy to learn shape concepts at both the geometry and appearance levels, decoupling the shape concept learning process. Third, we extend our approach to region-wise concept learning, enabling flexible concept extraction, with context-aware and context-free losses. Extensive experimental results show that PEGAsus is able to effectively extract attributes from a wide range of reference shapes and then flexibly compose these concepts with text to synthesize new shapes. This enables fine-grained control over shape generation and supports the creation of diverse, personalized results, even in challenging cross-category scenarios. Both quantitative and qualitative experiments demonstrate that our approach outperforms existing state-of-the-art solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08198 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PEGAsus: 3D Personalization of Geometry and Appearance Hu, Jingyu Hu, Bin Hui, Ka-Hei Li, Haipeng Liu, Zhengzhe Cohen-Or, Daniel Fu, Chi-Wing Computer Vision and Pattern Recognition Graphics We present PEGAsus, a new framework capable of generating Personalized 3D shapes by learning shape concepts at both Geometry and Appearance levels. First, we formulate 3D shape personalization as extracting reusable, category-agnostic geometric and appearance attributes from reference shapes, and composing these attributes with text to generate novel shapes. Second, we design a progressive optimization strategy to learn shape concepts at both the geometry and appearance levels, decoupling the shape concept learning process. Third, we extend our approach to region-wise concept learning, enabling flexible concept extraction, with context-aware and context-free losses. Extensive experimental results show that PEGAsus is able to effectively extract attributes from a wide range of reference shapes and then flexibly compose these concepts with text to synthesize new shapes. This enables fine-grained control over shape generation and supports the creation of diverse, personalized results, even in challenging cross-category scenarios. Both quantitative and qualitative experiments demonstrate that our approach outperforms existing state-of-the-art solutions. |
| title | PEGAsus: 3D Personalization of Geometry and Appearance |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2602.08198 |