Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2506.17212v1
• Date: Fri, 20 Jun 2025 17:59:12 GMT
• Title: Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
• Authors: Tianjiao Yu, Vedant Shah, Muntasir Wahed, Ying Shen, Kiet A. Nguyen, Ismini Lourentzou,
• Abstract summary: Part$2$GS is a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry.<n>To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints.<n>We show that Part$2$GS consistently outperforms state-of-the-art methods by up to 10$times$ in Chamfer Distance for movable parts.
• Score: 9.432567695900184
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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