  As 3D movie viewing becomes mainstream and Virtual Reality (VR) market
emerges, the demand for 3D contents is growing rapidly. Producing 3D videos,
however, remains challenging. In this paper we propose to use deep neural
networks for automatically converting 2D videos and images to stereoscopic 3D
format. In contrast to previous automatic 2D-to-3D conversion algorithms, which
have separate stages and need ground truth depth map as supervision, our
approach is trained end-to-end directly on stereo pairs extracted from 3D
movies. This novel training scheme makes it possible to exploit orders of
magnitude more data and significantly increases performance. Indeed, Deep3D
outperforms baselines in both quantitative and human subject evaluations.
