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arXiv · 2506.17212

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

Tianjiao Yu·Vedant Shah·Muntasir Wahed·Ying Shen·Kiet A. Nguyen·Ismini Lourentzou
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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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Tianjiao Yu, Vedant Shah, Muntasir Wahed, Ying Shen, Kiet A. Nguyen, Ismini Lourentzou. 2025-06-20. Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting. https://arxiv.org/abs/2506.17212

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