让3D形状生成支持个性定制，可自由组合几何与外观特征。
PEGAsus: 3D Personalization of Geometry and Appearance
- 从参考形状中提取通用几何与外观属性，用文本控制生成新形状。
- 通过渐进优化实现几何与外观概念解耦学习，提升生成灵活性。
- 支持局部区域概念提取，适合个性化3D设计与跨类别生成。
我们提出 PEGAsus，一种能够通过在几何和外观层面学习形状概念来生成个性化3D形状的新框架。首先，将3D形状个性化建模为从参考形状中提取可复用、类别无关的几何与外观属性，并结合文本生成新形状。其次，设计渐进优化策略，在几何与外观层面解耦地学习形状概念。第三，扩展至区域级概念学习，引入上下文感知与无上下文损失，实现灵活的概念提取。大量实验表明，PEGAsus能有效从多种参考形状中提取属性，并灵活组合这些概念生成新形状，实现对生成过程的细粒度控制，支持多样化个性化结果，即使在跨类别场景下也表现优异。定量与定性实验均证明该方法优于现有最先进方案。
原文摘要 · Abstract (English)
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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