PartSynth: Generative Sparse Part Structures for Compositional 3D Generation with PBR Materials

High-fidelity compositional 3D assets require separable parts, detailed geometry, and physically based rendering materials. Existing methods either constrain part counts or incur inference costs that grow with the number of parts. Monolithic material generators also lack explicit part awareness. We introduce PartSynth, an image-conditioned framework for compositional 3D geometry and native part-aware PBR generation. Its core representation, the Sparse Part Structure (SPS), uses reusable set-indexed latent slots to represent multi-part structure, jointly generating each slot's activity and feature. Multiple active slots at the same spatial location represent overlapping components, while reusing set indices across disconnected components supports flexible part counts. Connected components of the decoded set-indexed volumes define individual parts. The SPS-guided geometry and PBR stages adapt pretrained monolithic generators to produce detailed multi-part geometry with PBR materials. Experiments show improved part-level geometry and PBR material quality over part-generation baselines, while maintaining competitive whole-shape fidelity.