Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance
This addresses the need for high-quality, controllable 3D shape generation in domains like computer graphics and robotics, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of generating 3D point clouds by explicitly modeling part composition and symmetry, achieving state-of-the-art performance with guaranteed symmetry and coherent part placement.
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.