From One to More: Contextual Part Latents for 3D Generation

• URL: http://arxiv.org/abs/2507.08772v2
• Date: Thu, 30 Oct 2025 04:25:25 GMT
• Title: From One to More: Contextual Part Latents for 3D Generation
• Authors: Shaocong Dong, Lihe Ding, Xiao Chen, Yaokun Li, Yuxin Wang, Yucheng Wang, Qi Wang, Jaehyeok Kim, Chenjian Gao, Zhanpeng Huang, Zibin Wang, Tianfan Xue, Dan Xu,
• Abstract summary: CoPart is a part-aware diffusion framework that decomposes 3D objects into contextual part latents for coherent multi-part generation.<n>We construct a novel 3D part dataset derived from articulated mesh segmentation and human-verified annotations.<n>Experiments demonstrate CoPart's superior capabilities in part-level editing, object generation, and scene composition with unprecedented controllability.
• Score: 38.36190651170286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth data. Despite progress, three key limitations persist: (1) Single-latent representations fail to capture complex multi-part geometries, causing detail degradation; (2) Holistic latent coding neglects part independence and interrelationships critical for compositional design; (3) Global conditioning mechanisms lack fine-grained controllability. Inspired by human 3D design workflows, we propose CoPart - a part-aware diffusion framework that decomposes 3D objects into contextual part latents for coherent multi-part generation. This paradigm offers three advantages: i) Reduces encoding complexity through part decomposition; ii) Enables explicit part relationship modeling; iii) Supports part-level conditioning. We further develop a mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising, ensuring both geometric coherence and foundation model priors. To enable large-scale training, we construct Partverse - a novel 3D part dataset derived from Objaverse through automated mesh segmentation and human-verified annotations. Extensive experiments demonstrate CoPart's superior capabilities in part-level editing, articulated object generation, and scene composition with unprecedented controllability.

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