DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Authors: name: Tianjiao Yu, name: Xinzhuo Li, name: Muntasir Wahed, name: Jerry Xiong, name: Yifan Shen, name: Ying Shen, name: Ismini Lourentzou
Organization: PLAN Lab @University of Illinois Urbana-Champaign

Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional structure of parts. While recent part-aware approaches introduce decomposition, they remain largely geometry-focused, lacking semantic grounding and failing to model how parts align with textual descriptions or their inter-part relations. We propose DreamPartGen, a framework for semantically grounded, part-aware text-to-3D generation. DreamPartGen introduces Duplex Part Latents (DPLs) that jointly model each part's geometry and appearance, and Relational Semantic Latents (RSLs) that capture inter-part dependencies derived from language. A synchronized co-denoising process enforces mutual geometric and semantic consistency, enabling coherent, interpretable, and text-aligned 3D synthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity and text-shape alignment.