PEGAsus: 3D Personalization of Geometry and Appearance

• URL: http://arxiv.org/abs/2602.08198v1
• Date: Mon, 09 Feb 2026 01:41:27 GMT
• Title: PEGAsus: 3D Personalization of Geometry and Appearance
• Authors: Jingyu Hu, Bin Hu, Ka-Hei Hui, Haipeng Li, Zhengzhe Liu, Daniel Cohen-Or, Chi-Wing Fu,
• Abstract summary: PEGAsus is a new framework capable of generating Personalized 3D shapes by learning shape concepts at both Geometry and Appearance levels.<n>We formulate 3D shape personalization as extracting reusable, category-agnostic geometric and appearance attributes from reference shapes.<n>We extend our approach to region-wise concept learning, enabling flexible concept extraction, with context-aware and context-free losses.
• Score: 84.10611282310562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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