  We present a fully data-driven method to compute depth from diverse monocular
video sequences that contain large amounts of non-rigid objects, e.g., people.
In order to learn reconstruction cues for non-rigid scenes, we introduce a new
dataset consisting of stereo videos scraped in-the-wild. This dataset has a
wide variety of scene types, and features large amounts of nonrigid objects,
especially people. From this, we compute disparity maps to be used as
supervision to train our approach. We propose a loss function that allows us to
generate a depth prediction even with unknown camera intrinsics and stereo
baselines in the dataset. We validate the use of large amounts of Internet
video by evaluating our method on existing video datasets with depth
supervision, including SINTEL, and KITTI, and show that our approach
generalizes better to natural scenes.
