DreamPartGen：基于语义的部件级三维生成技术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，现有文本生成3D方法大多忽视部件的语义与功能结构。虽然近期部件感知方法引入了分解机制，但仍局限于几何层面，缺乏语义基础，无法建模部件与文本描述的对应关系及部件间关联。我们提出DreamPartGen框架，实现基于语义的部件感知式文本生成3D。该框架创新性地提出双工部件隐变量（DPL）联合建模各部件几何与外观特征，并构建关系语义隐变量（RSL）捕捉从语言推导的部件间依赖关系。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贴合文本的3D生成。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水平。

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.