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.

翻译：我们提出了四重扩散，一种通过显式建模部件组合与对称性的结构感知点云生成框架。与先前将形状生成视为整体过程或仅支持部件组合的方法不同，我们的方法利用四个协调的扩散模型来学习全局形状潜在变量、对称性、语义部件及其空间装配的分布。这种结构化流程确保了对称性的保证、部件放置的连贯性以及多样化的高质量输出。通过将生成过程解耦为可解释的组件，我们的方法支持对形状属性的细粒度控制，能够在保持全局一致性的同时，对单个部件进行针对性操作。一个核心的全局潜在变量进一步强化了装配部件间的结构一致性。我们的实验表明，四重扩散框架实现了最先进的性能。据我们所知，这是首个在生成过程中完全集成并强制执行对称性与部件先验的三维点云生成框架。