  We present a method for predicting dense depth in scenarios where both a
monocular camera and people in the scene are freely moving. Existing methods
for recovering depth for dynamic, non-rigid objects from monocular video impose
strong assumptions on the objects' motion and may only recover sparse depth. In
this paper, we take a data-driven approach and learn human depth priors from a
new source of data: thousands of Internet videos of people imitating
mannequins, i.e., freezing in diverse, natural poses, while a hand-held camera
tours the scene. Because people are stationary, training data can be generated
using multi-view stereo reconstruction. At inference time, our method uses
motion parallax cues from the static areas of the scenes to guide the depth
prediction. We demonstrate our method on real-world sequences of complex human
actions captured by a moving hand-held camera, show improvement over
state-of-the-art monocular depth prediction methods, and show various 3D
effects produced using our predicted depth.
