UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation

• URL: http://arxiv.org/abs/2511.21887v1
• Date: Wed, 26 Nov 2025 20:09:11 GMT
• Title: UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation
• Authors: Bu Jin, Weize Li, Songen Gu, Yupeng Zheng, Yuhang Zheng, Zhengyi Zhou, Yao Yao,
• Abstract summary: UniArt is a diffusion-based framework that synthesizes fully articulated 3D objects from a single image in an end-to-end manner.<n>We introduce a reversible joint-to-voxel embedding, which spatially aligns articulation features with volumetric geometry.<n>Experiments on the PartNet-Mobility benchmark demonstrate that UniArt achieves state-of-the-art mesh quality and articulation accuracy.
• Score: 14.687459506970301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Articulated 3D objects play a vital role in realistic simulation and embodied robotics, yet manually constructing such assets remains costly and difficult to scale. In this paper, we present UniArt, a diffusion-based framework that directly synthesizes fully articulated 3D objects from a single image in an end-to-end manner. Unlike prior multi-stage techniques, UniArt establishes a unified latent representation that jointly encodes geometry, texture, part segmentation, and kinematic parameters. We introduce a reversible joint-to-voxel embedding, which spatially aligns articulation features with volumetric geometry, enabling the model to learn coherent motion behaviors alongside structural formation. Furthermore, we formulate articulation type prediction as an open-set problem, removing the need for fixed joint semantics and allowing generalization to novel joint categories and unseen object types. Experiments on the PartNet-Mobility benchmark demonstrate that UniArt achieves state-of-the-art mesh quality and articulation accuracy.

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