We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.
Paper pdf, to appear in CVPR 2018!
@inproceedings{rematasCVPR18, author = {Konstantinos Rematas and Ira Kemelmacher-Shlizerman and Brian Curless and Steve Seitz }, title = {Soccer On Your Tabletop}, year = {2018}, booktitle = {CVPR} }