Learning the Depths of Moving People by Watching Frozen People
May 28, 2019
Learning the Depths of Moving People by Watching Frozen People
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Li, Zhengqi;
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Dekel, Tali;
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Cole, Forrester;
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Tucker, Richard;
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Snavely, Noah;
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Liu, Ce;
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Freeman, William T.;
Abstract: 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.
https://arxiv.org/abs/1904.11111
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