UniPart：基于统一三维几何-分割潜变量的部件级三维生成
UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents
Geom-Seg VecSet 把物体几何与部件级结构编码进统一的潜变量表示，使部件感知在整体几何学习过程中自然涌现，从而无需依赖外部强分割器；做三维生成或部件编辑的工作可以直接复用这一潜变量表示与两阶段潜扩散流程。
原文依据 part awareness emerges naturally during whole-object geometry learning and propose Geom-Seg VecSet
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论部件级三维生成对于需要可分解、结构化三维合成的应用至关重要。然而，现有方法要么依赖粒度控制有限的隐式部件分割，要么依赖在大规模标注数据集上训练的强外部分割器。在本工作中，我们观察到部件感知会在整体物体几何学习过程中自然涌现，并提出 Geom-Seg VecSet——一种统一编码物体几何与部件级结构的几何-分割潜变量表示。基于该表示，我们提出 UniPart，一个用于图像引导部件级三维生成的两阶段潜扩散框架。第一阶段联合执行几何生成与潜空间部件分割，第二阶段则将部件级扩散同时以整体物体潜变量和部件特定潜变量为条件。双空间生成方案进一步通过在全局空间与规范空间中同时预测部件潜变量来提升几何保真度。大量实验表明，与现有方法相比，UniPart 实现了更优的分割可控性与部件级几何质量。
英文原文摘要 1146 字符
Part-level 3D generation is essential for applications requiring decomposable and structured 3D synthesis. However, existing methods either rely on implicit part segmentation with limited granularity control or depend on strong external segmenters trained on large annotated datasets. In this work, we observe that part awareness emerges naturally during whole-object geometry learning and propose Geom-Seg VecSet, a unified geometry-segmentation latent representation that jointly encodes object geometry and part-level structure. Building on this representation, we introduce UniPart, a two-stage latent diffusion framework for image-guided part-level 3D generation. The first stage performs joint geometry generation and latent part segmentation, while the second stage conditions part-level diffusion on both whole-object and part-specific latents. A dual-space generation scheme further enhances geometric fidelity by predicting part latents in both global and canonical spaces. Extensive experiments demonstrate that UniPart, achieves superior segmentation controllability and part-level geometric quality compared with existing approaches.