DreamPartGen：基于语义的部件级三维生成——通过协同隐空间去噪实现

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

March 19, 2026

作者: Tianjiao Yu, Xinzhuo Li, Muntasir Wahed, Jerry Xiong, Yifan Shen, Ying Shen, Ismini Lourentzou

cs.AI

摘要

理解并生成具有可解释部件结构的3维物体是人类感知与推理的基础能力。然而多数文本生成3D方法忽视了部件的语义与功能结构。尽管近期部分感知方法引入了分解机制，但仍主要聚焦几何特征，缺乏语义基础，未能建模部件如何与文本描述对齐及其相互关系。我们提出DreamPartGen框架，实现基于语义的部件感知文本生成3D。该框架创新性地引入双通道部件潜变量（DPL）联合建模各部件几何形态与外观特征，并通过关系语义潜变量（RSL）捕捉语言驱动的部件间依赖关系。通过同步协同去噪过程强化几何与语义的互一致性，最终实现连贯、可解释且文本对齐的3D合成。在多项基准测试中，DreamPartGen在几何保真度与文本-形状对齐方面均达到最先进性能。

English

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.