通过四扩散模型实现对称与部件结构的精准生成。
Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance
- 用四个协同扩散模型分别建模全局结构、对称性、语义部件和空间组合。
- 生成结果兼具对称性、部件合理布局和高质量多样性，优于现有方法。
- 适合需要精细控制部件属性且保持整体一致性的3D设计场景。
我们提出Quartet of Diffusions，一种结构感知的点云生成框架，显式建模部件组成与对称性。不同于以往将形状生成视为整体过程或仅支持部件组合的方法，本框架利用四个协调的扩散模型，学习全局形状潜在变量、对称性、语义部件及其空间组装的分布。该结构化流程确保了对称性保障、部件合理布局以及多样且高质量的输出。通过将生成过程解耦为可解释的组件，方法支持对形状属性的细粒度控制，可在调整单个部件时保持全局一致性。一个核心全局潜在变量进一步强化了组装部件间的结构一致性。实验表明，Quartet达到当前最优性能。据我们所知，这是首个在生成过程中完全整合并强制执行对称性和部件先验的3D点云生成框架。
原文摘要 · Abstract (English)
We introduce the Quartet of Diffusions, a structure-aware point cloud generation framework that explicitly models part composition and symmetry. Unlike prior methods that treat shape generation as a holistic process or only support part composition, our approach leverages four coordinated diffusion models to learn distributions of global shape latents, symmetries, semantic parts, and their spatial assembly. This structured pipeline ensures guaranteed symmetry, coherent part placement, and diverse, high-quality outputs. By disentangling the generative process into interpretable components, our method supports fine-grained control over shape attributes, enabling targeted manipulation of individual parts while preserving global consistency. A central global latent further reinforces structural coherence across assembled parts. Our experiments show that the Quartet achieves state-of-the-art performance. To our best knowledge, this is the first 3D point cloud generation framework that fully integrates and enforces both symmetry and part priors throughout the generative process.
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