用统一隐空间实现可控的部件级3D生成，无需外部标注数据。
UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents
- 提出几何-分割联合隐表示，自动捕捉部件结构。
- 两阶段扩散框架生成高质量部件级3D模型，可调性更强。
- 适合需要精细结构控制的3D内容生成任务。
部件级3D生成对可分解、结构化合成应用至关重要。现有方法或依赖隐式分割且粒度难控，或需大量标注数据训练外部分割器。本文观察到部件感知在整体几何学习中自然涌现，提出Geom-Seg VecSet——一种统一编码物体几何与部件结构的隐表示。基于此，构建UniPart：一个两阶段潜空间扩散框架，支持图像引导的部件级3D生成。第一阶段联合生成几何并进行潜空间部件分割；第二阶段同时使用全局与部件特定隐向量条件化扩散过程。双空间生成策略通过在全局与规范空间中预测部件隐向量，进一步提升几何保真度。大量实验表明，相比现有方法，UniPart在分割可控性与部件级几何质量上均更优。
原文摘要 · Abstract (English)
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.
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