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We present a system that transforms a monocular video of a soccer game into amoving 3D reconstruction, in which the players and field can be renderedinteractively with a 3D viewer or through an Augmented Reality device. At theheart 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 videogames. We compare with state of the art body pose and depth estimationtechniques, and show results on both synthetic ground truth benchmarks, andreal YouTube soccer footage.

Soccer on Your Tabletop