The optical flow of humans is well known to be useful for the analysis of
human action. Given this, we devise an optical flow algorithm specifically for
human motion and show that it is superior to generic flow methods. Designing a
method by hand is impractical, so we develop a new training database of image
sequences with ground truth optical flow. For this we use a 3D model of the
human body and motion capture data to synthesize realistic flow fields. We then
train a convolutional neural network to estimate human flow fields from pairs
of images. Since many applications in human motion analysis depend on speed,
and we anticipate mobile applications, we base our method on SpyNet with
several modifications. We demonstrate that our trained network is more accurate
than a wide range of top methods on held-out test data and that it generalizes
well to real image sequences. When combined with a person detector/tracker, the
approach provides a full solution to the problem of 2D human flow estimation.
Both the code and the dataset are available for research.