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.

翻译：我们提出了PEGAsus，一种能够通过在几何和外观两个层面学习形状概念来生成个性化三维形状的新框架。首先，我们将三维形状个性化定义为从参考形状中提取可复用的、类别无关的几何与外观属性，并将这些属性与文本描述相结合以生成新颖形状。其次，我们设计了一种渐进式优化策略，在几何和外观两个层面学习形状概念，从而解耦了形状概念的学习过程。第三，我们将该方法扩展到区域级概念学习，通过上下文感知与上下文无关的损失函数，实现灵活的概念提取。大量实验结果表明，PEGAsus能够有效地从广泛的参考形状中提取属性，并灵活地将这些概念与文本结合以合成新形状。这使得对形状生成过程实现了细粒度控制，并支持创建多样化的个性化结果，即使在具有挑战性的跨类别场景中也是如此。定量与定性实验均表明，我们的方法优于现有的最先进解决方案。