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.
OmniPart generates part-aware, controllable, and high-quality 3D content through two key stages: part structure planning and structured part latent generation.
Built upon TRELLIS, which provides a spatially structured sparse voxel latent space, OmniPart first predicts part-level bounding boxes via an auto-regressive planner.
Then, part-specific latent codes are generated through fine-tuning of a large-scale shape model pretrained on overall objects.
OmniPart generates high-quality part-aware 3D content directly from a single input image and naturally supports a range of downstream applications, including Animation, Mask-Controlled Generation, Multi-Granularity Generation, Material Editing, and Geometry Processing.