High Fidelity Facial Hair Capture

We propose an extension to multi-view face capture that reconstructs high quality facial hair automatically. Multi-view stereo is well known for producing high quality smooth surfaces and meshes, but fails on fine structure such as hair. We exploit this failure, and automatically detect the hairs on a face by careful analysis of the pixel reconstruction error of the multi-view stereo result. Central to our work is a novel stereo matching cost function, which we call equalized cross correlation, that properly accounts for both camera sensor noise and pixel sampling variance. In contrast to previous works that treat hair modeling as a synthesis problem based on image cues, we reconstruct facial hair to explain the same high-resolution input photographs used for face reconstruction, producing a result with higher fidelity to the input photographs.