Dynamic Hair Capture
Princeton University, August 2011
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.
Paper
Video
- MOV (235 MB)
Links
- Princeton CS tech report
- Project page by Hao Li
Citation
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.
BibTeX
@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" }