让静态3D模型自动变出会动的仿真数字孪生，不穿模也不出错。
MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins
- 用物理先验约束视觉语言模型，避免关节幻觉。
- 联合优化几何与物理，确保运动时零件不穿插。
- 零样本输入，直接生成可模拟的高精度数字孪生。
将静态3D网格转化为可交互的关节化资产对具身AI和机器人仿真至关重要。然而，现有零样本流程在处理复杂资产时因缺乏物理根基而表现不佳：无根基的视觉-语言模型常产生错误关节配置，而无约束的关节估计则导致物理仿真中出现灾难性网格穿透。为此，我们提出MotionAnymesh，一种自动化零样本框架，可将非结构化静态网格无缝转换为仿真就绪的数字孪生。该方法包含一个基于运动学感知的部件分割模块，通过显式的SP4D物理先验来锚定VLM推理，有效消除运动学幻觉；同时引入几何-物理联合估计流程，结合类型感知初始化与物理约束轨迹优化，严格保证无碰撞关节运动。大量实验表明，MotionAnymesh在几何精度和动态物理可执行性上显著优于当前最优基线，为下游应用提供高度可靠的资产。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。