Temporal Upsampling of Performance Geometry Using Photometric Alignment
Temporal Upsampling of Performance Geometry Using Photometric Alignment
ACM Transactions on Graphics, Volume 29, Issue 2 (March 2010)
Cyrus A. Wilson   Abhijeet Ghosh   Pieter Peers   Jen-Yuan Chiang   Jay Busch   Paul Debevec
USC Institute for Creative Technologies

Smiling sequence captured under extended spherical gradient illumination conditions (top row), synthesized intermediate photometric normals (center row), and high resolution geometry (bottom row) of a facial performance as reconstructed with the same temporal resolution as the data capture.



We present a novel technique for acquiring detailed facial geometry of a dynamic performance using extended spherical gradient illumination. Key to our method is a new algorithm for jointly aligning two photographs - under a gradient illumination condition and its complement - to a full-on tracking frame, providing dense temporal correspondences under changing lighting conditions. We employ a two step algorithm to reconstruct detailed geometry for every captured frame. In the first step, we coalesce information from the gradient illumination frames to the full-on tracking frame, and form a temporally aligned photometric normal map, which is subsequently combined with dense stereo correspondences yielding a detailed geometry. In a second step, we propagate the detailed geometry back to every captured instance guided by the previously computed dense correspondences. We demonstrate reconstructed dynamic facial geometry, captured using moderate to video rates of acquisition, for every captured frame.



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