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Multi-View Hair Capture Using Orientation Fields
Computer Vision and Pattern Recognition (CVPR), June 2012

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


Abstract

Reconstructing realistic 3D hair geometry is challenging due to omnipresent occlusions, complex discontinuities and specular appearance. To address these challenges, we propose a multi-view hair reconstruction algorithm based on orientation fields with structure-aware aggregation. Our key insight is that while hairís color appearance is view-dependent, the response to oriented filters that captures the local hair orientation is more stable. We apply the structure-aware aggregation to the MRF matching energy to enforce the structural continuities implied from the local hair orientations. Multiple depth maps from the MRF optimization are then fused into a globally consistent hair geometry with a template refinement procedure. Compared to the state-of-the-art color-based methods, our method faithfully reconstructs detailed hair structures. We demonstrate the results for a number of hair styles, ranging from straight to curly, and show that our framework is suitable for capturing hair in motion.

Citation (BibTeX)

Linjie Luo, Hao Li, Sylvain Paris, Thibaut Weise, Mark Pauly, and Szymon Rusinkiewicz. Multi-View Hair Capture Using Orientation Fields. Computer Vision and Pattern Recognition (CVPR), June 2012.

Paper
  PDF file

Video
  MOV video (4:02, 103 MB, H.264, with audio)

Links
  Video on Youtube
  Hao Li's project page for this paper
  Earlier work on hair capture as a tech report

Poster
  PDF file