CVJul 8, 2025

OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion

arXiv:2507.06165v140 citationsh-index: 10SIGGRAPH Asia
Originality Highly original
AI Analysis

This addresses the need for more interpretable and editable 3D content in interactive applications, representing a novel method rather than an incremental improvement.

The paper tackles the problem of generating 3D assets with explicit, editable part structures, which is crucial for interactive applications, by introducing OmniPart, a framework that achieves high semantic decoupling and structural cohesion, resulting in state-of-the-art performance.

The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, limiting their utility. We introduce OmniPart, a novel framework for part-aware 3D object generation designed to achieve high semantic decoupling among components while maintaining robust structural cohesion. OmniPart uniquely decouples this complex task into two synergistic stages: (1) an autoregressive structure planning module generates a controllable, variable-length sequence of 3D part bounding boxes, critically guided by flexible 2D part masks that allow for intuitive control over part decomposition without requiring direct correspondences or semantic labels; and (2) a spatially-conditioned rectified flow model, efficiently adapted from a pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and consistently within the planned layout. Our approach supports user-defined part granularity, precise localization, and enables diverse downstream applications. Extensive experiments demonstrate that OmniPart achieves state-of-the-art performance, paving the way for more interpretable, editable, and versatile 3D content.

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