A part-aware diffusion framework, CoPart, enhances 3D generation by decomposing objects into contextual parts, improving complexity handling, relationship modeling, and part-level conditioning.
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.