From One to More: Contextual Part Latents for 3D Generation
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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Shaocong Dong
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Lihe Ding
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Xiao Chen
68746c95257d4f04353702ba
Yaokun Li
68746c95257d4f04353702bb
Yuxin Wang
68746c95257d4f04353702bc
Yucheng Wang
68746c95257d4f04353702bd
Qi Wang
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Jaehyeok Kim
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Chenjian Gao
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Zhanpeng Huang
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Zibin Wang
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Tianfan Xue
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Dan Xu
claimed_verified
2025-07-15T19:10:43.038Z
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/avatars/7344377e2c796c7ec85194bb2fc78521.svg
Dan Xu
user
danxuhk
From One to More: Contextual Part Latents for 3D Generation
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.
CoPart, a part-aware diffusion framework, enhances 3D generation by decomposing objects into contextual part latents, improving detail, part relationships, and controllability.
latent diffusion frameworks geometric priors single-latent representations holistic latent coding part independence interrelationships compositional design global conditioning mechanisms part-aware diffusion framework part decomposition explicit part relationship modeling part-level conditioning mutual guidance strategy pre-trained diffusion models joint part latent denoising geometric coherence Partverse Objaverse automated mesh segmentation human-verified annotations part-level editing articulated object generation scene composition