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Dynamic Hair Capture
Princeton University, August 2011

Linjie Luo, Hao Li, Thibaut Weise,
Sylvain Paris, Mark Pauly, Szymon Rusinkiewicz


Abstract

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.

Citation (BibTeX)

Linjie Luo, Hao Li, Thibaut Weise, Sylvain Paris, Mark Pauly, and Szymon Rusinkiewicz. Dynamic Hair Capture. Technical Report TR-907-11, Princeton University, August 2011.

Paper
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Video
  MOV (235 MB)

Links
  Princeton CS tech report
  Project page by Hao Li