Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
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arXiv
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| Format: | Preprint |
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2025
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| author | Yu, Tianjiao Shah, Vedant Wahed, Muntasir Shen, Ying Nguyen, Kiet A. Lourentzou, Ismini |
| author_facet | Yu, Tianjiao Shah, Vedant Wahed, Muntasir Shen, Ying Nguyen, Kiet A. Lourentzou, Ismini |
| contents | Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$^{2}$GS, a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry and physically consistent articulation. Part$^{2}$GS leverages a part-aware 3D Gaussian representation that encodes articulated components with learnable attributes, enabling structured, disentangled transformations that preserve high-fidelity geometry. To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints, including contact enforcement, velocity consistency, and vector-field alignment. Furthermore, we introduce a field of repel points to prevent part collisions and maintain stable articulation paths, significantly improving motion coherence over baselines. Extensive evaluations on both synthetic and real-world datasets show that Part$^{2}$GS consistently outperforms state-of-the-art methods by up to 10$\times$ in Chamfer Distance for movable parts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting Yu, Tianjiao Shah, Vedant Wahed, Muntasir Shen, Ying Nguyen, Kiet A. Lourentzou, Ismini Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$^{2}$GS, a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry and physically consistent articulation. Part$^{2}$GS leverages a part-aware 3D Gaussian representation that encodes articulated components with learnable attributes, enabling structured, disentangled transformations that preserve high-fidelity geometry. To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints, including contact enforcement, velocity consistency, and vector-field alignment. Furthermore, we introduce a field of repel points to prevent part collisions and maintain stable articulation paths, significantly improving motion coherence over baselines. Extensive evaluations on both synthetic and real-world datasets show that Part$^{2}$GS consistently outperforms state-of-the-art methods by up to 10$\times$ in Chamfer Distance for movable parts. |
| title | Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2506.17212 |