发表机构
ShanghaiTech University（上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出智能体框架FACT，通过证据-诊断-修订循环逐步构建铰接孪生，提升几何、接触和动态保真度，实验证明其在重建、交互和物理响应方面优于基线。
AI 中文摘要
视觉上合理的铰接资产在接触交互中仍可能失败或表现出不准确的运动。我们提出了FACT（保真度感知的铰接孪生构建），一个智能体框架，通过逐步构建铰接孪生来提高几何、接触和动态保真度。该智能体在共享的可编辑表示上驱动一个证据-诊断-修订循环，利用定量反馈选择测量和模型编辑，而数值工具则执行并验证更新。它通过特征规划、定向测量和诊断性细化从图像中重建可编辑的铰接几何。在此参考上，它通过任务感知的局部重新划分修复碰撞代理，然后进行保真度约束的压缩。最后，它从被动响应视频中构建响应模型，利用模拟残差指导模型修订和受约束的物理参数拟合。实验表明，FACT在几何重建上优于基线，能够使用更简单的碰撞代理实现更可靠的交互，并且比直接参数推断更好地再现保留的物理响应。
英文摘要
Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Construction of Articulated Twins), an agentic framework that progressively constructs articulated twins to improve geometry, contact, and dynamic fidelity. The agent drives an evidence--diagnosis--revision loop on a shared editable representation, selecting measurements and model edits using quantitative feedback, while numerical tools execute and validate the updates. It reconstructs editable articulated geometry from images through feature planning, targeted measurements, and diagnostic refinement. On this reference, it repairs collision proxies through task-aware local repartitioning before fidelity-constrained compression. Finally, it constructs response models from passive-response videos, using simulation residuals to guide model revision and constrained physical parameter fitting. Experiments show that FACT improves geometric reconstruction over baselines, enables more reliable interaction with simpler collision proxies, and better reproduces held-out physical responses than direct parameter inference.
