MannequinChallenge: Learning the Depths of Moving People by Watching Frozen People.

We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving (right). 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.

Two fast methods for high-quality line visibility.

Lines drawn over or in place of shaded 3D models can often provide greater comprehensibility and stylistic freedom than shading alone. A substantial challenge for making stylized line drawings from 3D models is the visibility computation. Current algorithms for computing line visibility in models of moderate complexity are either too slow for interactive rendering, or too brittle for coherent animation.