Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance

2026-01-28

AI Summary

Existing methods for 3D point cloud generation struggle to simultaneously model part composition and symmetry, lacking structural awareness and fine-grained control. This work proposes the first quadruple diffusion framework that explicitly integrates symmetry and semantic part priors throughout the entire generation process, separately modeling global shape latent variables, symmetry attributes, part geometry, and their spatial assembly. The approach achieves, for the first time, structure-aware, interpretable, and controllable part-level point cloud generation, enabling fine-grained editing. Experiments demonstrate that the method sets a new state of the art in generation quality, structural consistency, and diversity, significantly outperforming existing approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• point cloud generation
• structure-aware
• symmetry
• part composition
• 3D shape modeling

Innovation

• structure-aware generation
• point cloud diffusion
• part-based modeling
• symmetry enforcement
• disentangled generative process