MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins

• URL: http://arxiv.org/abs/2603.12936v1
• Date: Fri, 13 Mar 2026 12:30:42 GMT
• Title: MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins
• Abstract summary: MotionAnymesh is a framework that seamlessly transforms unstructured static meshes into simulation-ready digital twins.<n>Our method features a kinematic-aware part segmentation module that grounds VLM reasoning with explicit SP4D physical priors.
• Score: 25.25391483526521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Converting static 3D meshes into interactable articulated assets is crucial for embodied AI and robotic simulation. However, existing zero-shot pipelines struggle with complex assets due to a critical lack of physical grounding. Specifically, ungrounded Vision-Language Models (VLMs) frequently suffer from kinematic hallucinations, while unconstrained joint estimation inevitably leads to catastrophic mesh inter-penetration during physical simulation. To bridge this gap, we propose MotionAnymesh, an automated zero-shot framework that seamlessly transforms unstructured static meshes into simulation-ready digital twins. Our method features a kinematic-aware part segmentation module that grounds VLM reasoning with explicit SP4D physical priors, effectively eradicating kinematic hallucinations. Furthermore, we introduce a geometry-physics joint estimation pipeline that combines robust type-aware initialization with physics-constrained trajectory optimization to rigorously guarantee collision-free articulation. Extensive experiments demonstrate that MotionAnymesh significantly outperforms state-of-the-art baselines in both geometric precision and dynamic physical executability, providing highly reliable assets for downstream applications.

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