DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising

摘要

Understanding and generating 3D objects as compositionsof meaningful parts is fundamental to human perception and reasoning.However, most text-to-3D methods overlook the semantic and functionalstructure of parts. While recent part-aware approaches introduce de-composition, they remain largely geometry-focused, lacking semanticgrounding and failing to model how parts align with textual descriptionsor their inter-part relations. We propose DreamPartGen, a frameworkfor semantically grounded, part-aware text-to-3D generation. Dream-PartGen introduces Duplex Part Latents (DPLs) that jointly modeleach part’s geometry and appearance, and Relational Semantic Latents(RSLs) that capture inter-part dependencies derived from language. Asynchronized co-denoising process enforces mutual geometric and seman-tic consistency, enabling coherent, interpretable, and text-aligned 3Dsynthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity (→60% Chamfer Distance) andtext–shape alignment (↑↓20% CLIP/ULIP), while producing composi-tionally consistent and controllable parts.PLAN Lab https://plan-lab.github.io/dreampartgen