Improving Dense Image Correspondence Estimation with Interactive User Guidance
ACM Multimedia (2012)
We present "Interactive Optical Flow Estimation" -- or longer "Improving Dense Image Correspondence Estimation with Interactive User Guidance", where we take a realtime GPU per-pixel motion estimator and allow users to interactively fix errors of automated algorithms.
High quality dense image correspondence estimation between two images is an essential prerequisite for view interpolation in visual media production. Due to the ill-posed nature of the problem, automated estimation approaches are prone to erroneous correspondences and subsequent quality degradation, e.g. in the presence of ambiguous movements that require human scene understanding to resolve. Where visually convincing results are essential, artifacts resulting from estimation errors must be repaired by hand with image editing tools. In this paper, we propose a new workflow alternative by fixing the correspondences instead of fixing the interpolated images. We combine realtime interactive correspondence display, multi-level user guidance and algorithmic subpixel precision to counteract failure cases of automated estimation algorithms. Our results show that already few interactions improve the visual quality considerably.
Kai Ruhl, Benjamin Hell, Felix Klose, Christian Lipski, Sören Petersen, Marcus Magnor (2012). Improving Dense Image Correspondence Estimation with Interactive User Guidance. Proceedings of ACM Multimedia 2012.
@inproceedings{ruhl2012acmmm,
author = {Ruhl, Kai and Hell, Benjamin and Klose, Felix and Lipski, Christian and Petersen, Soren and Magnor, Marcus},
title = {Improving Dense Image Correspondence Estimation with Interactive User Guidance},
booktitle = {Proceedings of {ACM} Multimedia 2012},
publisher = {{ACM}},
pages = {1129--1132},
month = oct,
year = {2012}
}