Carnegie Mellon University
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Super-Resolution Optical Flow
This project is no longer active.
Head: Simon Baker
Contact: Simon Baker
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Existing approaches to super-resolution are not applicable to videos of faces because faces are non-planar, non-rigid, non-lambertian, and are subject to self occlusion. We propose super-resolution optical flow as a solution to these problems. Super-resolution optical flow takes as input a conventional video stream, and simultaneously computes both optical flow and a super-resolution version of the entire video. An example of the algorithm is included below. The input consists of a short video, 5 frames of which are:

The output of our algorithm on those 5 frames is:

A complete video output is: