The realistic reconstruction of hair motion is challenging because of hair's complex occlusion, lack of a well-defined surface, and non-Lambertian material. We present a system for passive capture of dynamic hair performances using a set of high-speed video cameras. Our key insight is that, while hair color is unlikely to match across multiple views, the response to oriented filters will. We combine a multi-scale version of this orientation-based matching metric with bilateral aggregation, a MRF-based stereo reconstruction technique, and algorithms for temporal tracking and de-noising. Our final output is a set of hair strands for each frame, grown according to the per-frame reconstructed rough geometry and orientation field. We demonstrate results for a number of hair styles ranging from smooth and ordered to curly and messy.

@techreport{Luo:2011:DHC,
   author = "Linjie Luo and Hao Li and Thibaut Weise and Sylvain Paris and Mark
      Pauly and Szymon Rusinkiewicz",
   title = "Dynamic Hair Capture",
   institution = "Princeton University",
   year = "2011",
   month = aug,
   number = "TR-907-11"
}