PRISM用概率模型融合几何与语义，生成结构更合理的3D形状。
PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation
- 将统计形体模型与高斯混合模型结合，分层建模部件几何与语义
- 在复杂多部件形状生成中实现更高保真度与多样性
- 适合需要精细控制部件结构与语义的3D生成任务
尽管3D完整形状生成已取得进展，但准确建模复杂形状的几何与语义特征，特别是部件数量不固定的情况，仍是重大挑战。现有方法难以有效将3D形状的上下文与结构信息融入生成过程。我们提出PRISM，一种新型组合式3D形状生成方法，将分类扩散模型与统计形体模型（SSM）及高斯混合模型（GMM）相结合。该方法利用组合式SSM捕捉部件级别的几何变化，使用GMM在连续空间中表示部件语义。这种融合使生成形状兼具高保真度与多样性，同时保持结构一致性。在形状生成与操作任务上的大量实验表明，该方法在生成质量与部件级操作可控性上显著优于先前方法。代码将公开发布。
原文摘要 · Abstract (English)
Despite the advancements in 3D full-shape generation, accurately modeling complex geometries and semantics of shape parts remains a significant challenge, particularly for shapes with varying numbers of parts. Current methods struggle to effectively integrate the contextual and structural information of 3D shapes into their generative processes. We address these limitations with PRISM, a novel compositional approach for 3D shape generation that integrates categorical diffusion models with Statistical Shape Models (SSM) and Gaussian Mixture Models (GMM). Our method employs compositional SSMs to capture part-level geometric variations and uses GMM to represent part semantics in a continuous space. This integration enables both high fidelity and diversity in generated shapes while preserving structural coherence. Through extensive experiments on shape generation and manipulation tasks, we demonstrate that our approach significantly outperforms previous methods in both quality and controllability of part-level operations. Our code will be made publicly available.
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