PEGAsus: 3D Personalization of Geometry and Appearance
This addresses the need for flexible and personalized 3D shape generation in computer graphics and AI, though it appears incremental as it builds on existing shape concept learning methods.
The paper tackles the problem of generating personalized 3D shapes by extracting reusable geometric and appearance attributes from reference shapes and composing them with text, resulting in a framework that outperforms state-of-the-art methods in fine-grained control and cross-category scenarios.
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.