Estimating the Material Properties of Fabric from Video

Abstract

Figure 1. A sample of the fabrics in our collected database ranked according to stiffness predicted by our model. The top panel shows physical fabric samples hanging from a rod. The bottom panel shows a horizontal space × time slice of a video when the fabrics are blown by the same wind intensity. Bendable fabrics generally contain more high frequency motion than stiff fabrics.

Visual vibrometry: Estimating material properties from small motions in video

“…Early approaches worked by fitting the parameters of cloth-specific models to video and depth information [2,17]. Bouman et al [3] adopted a learning approach that allowed them to estimate material properties from a video of fabric moving under wind forces. As with our experiments in Section 7, they estimate material properties directly from video statistics using a regression strategy.…”

“…In the work of Bouman, et al [3], a dataset of 30 fabrics along with ground truth measurements of stiffness and area weight were collected. We extend this dataset to predict the material properties from videos exhibiting small motions that are often invisible to the naked eye, in contrast to [3] that relied on much larger motions produced by fans.…”

“…Setup: Each fabric specimen from [3] (width 43.5 to 44.5 inches across) was loosely draped over a bar and hung a length of 29.25 to 32.25 inches from the top of the bar. Notice that although the geometry was kept relatively constant, these measurements vary a great deal compared to those used in Section 6.…”

“…We learn regression models that map the motion spectra to the log of ground truth stiffness or area weight measurements provided in [3]. Models are fit to the log of measurements in order to directly compare with results presented in [3].…”

“…Models are fit to the log of measurements in order to directly compare with results presented in [3]. Fitting a regression model directly to the processed motion spectra results in overfitting.…”