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

Converting static 3D meshes into interactable articulated as-sets is crucial for embodied AI and robotic simulation. However, exist-ing zero-shot pipelines struggle with complex assets due to a criticallack of physical grounding. Specifically, ungrounded Vision-LanguageModels (VLMs) frequently suffer from kinematic hallucinations, whileunconstrained joint estimation inevitably leads to catastrophic meshinter-penetration during physical simulation. To bridge this gap, we pro-pose MotionAnymesh, an automated zero-shot framework that seam-lessly transforms unstructured static meshes into simulation-ready dig-ital twins. Our method features a kinematic-aware part segmentationmodule that grounds VLM reasoning with explicit SP4D physical priors,effectively eradicating kinematic hallucinations. Furthermore, we intro-duce a geometry-physics joint estimation pipeline that combines robusttype-aware initialization with physics-constrained trajectory optimiza-tion to rigorously guarantee collision-free articulation. Extensive experi-ments demonstrate that MotionAnymesh significantly outperforms state-of-the-art baselines in both geometric precision and dynamic physicalexecutability, providing highly reliable assets for downstream applica-tions. Our project page is available at https://xwb0117.github.io/Motionanymesh/