For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, measuring physical properties from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. In this paper, we propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The correspondence is measured using an embedding function that maps physically similar examples to nearby points. We consider a case study of cloth in the wind, with curling flags as our leading example -- a seemingly simple phenomena but physically highly involved. Based on the physics of cloth and its visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. Our experiments demonstrate that the proposed method compares favorably to prior work on the task of measuring cloth material properties and external wind force from a real-world video.

本文提出了一种用于测量微风中布料潜在物理特性的迭代反复优化过程算法，其中心思想为通过模拟观察现象并比较当前模拟与真实世界的差异来更新物理模型参数，以模拟布料在风中的运动，采用一种嵌入函数确定相似性，并引入谱层将视频容量分解为其时间谱功率和相应频率。实验证明该方法比基于真实世界视频的布料材料属性和外部风力测量任务的之前的工作表现更好。

风中的布料：基于模拟的物理测量案例研究