. 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. Passively estimating the intrinsic material properties of deformable objects moving in a natural environment is essential for scene understanding. We present a framework to automatically analyze videos of fabrics moving under various unknown wind forces, and recover two key material properties of the fabric: stiffness and area weight. We extend features previously developed to compactly represent static image textures to describe video textures, such as fabric motion. A discriminatively trained regression model is then used to predict the physical properties of fabric from these features. The success of our model is demonstrated on a new, publicly available database of fabric videos with corresponding measured ground truth material properties. We show that our predictions are well correlated with ground truth measurements of stiffness and density for the fabrics. Our contributions include: (a) a database that can be used for training and testing algorithms for passively predicting fabric properties from video, (b) an algorithm for predicting the material properties of fabric from a video, and (c) a perceptual study of humans' ability to estimate the material properties of fabric from videos and images.
Bouman et al. REF measured fabric properties (stiffness and density) directly from the video of fabric motion using hand-crafted features, when the fabrics were hanged and exposed to different winds.
Estimating the Material Properties of Fabric from Video
2013 IEEE International Conference on Computer Vision
2013 IEEE International Conference on Computer Vision
Computer Science