Estimating the Material Properties of Fabric from Video
Name
Freeman_Estimating the.pdf
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3.3 MB
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Author(s)
Bouman, Katherine L.
Xiao, Bei
Freeman, William T.
Battaglia, Peter W.
Date Issued
December 2013
Journal
Proceedings of the 2013 IEEE International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bouman, Katherine L., Bei Xiao, Peter Battaglia, and William T. Freeman. “Estimating the Material Properties of Fabric from Video.” 2013 IEEE International Conference on Computer Vision (December 2013).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICCV.2013.455
