  The optical flow of humans is well known to be useful for the analysis of
human action. Recent optical flow methods focus on training deep networks to
approach the problem. However, the training data used by them does not cover
the domain of human motion. Therefore, we develop a dataset of multi-human
optical flow and train optical flow networks on this dataset. We use a 3D model
of the human body and motion capture data to synthesize realistic flow fields
in both single- and multi-person images. We then train optical flow networks to
estimate human flow fields from pairs of images. We demonstrate that our
trained networks are more accurate than a wide range of top methods on held-out
test data and that they can generalize well to real image sequences. The code,
trained models and the dataset are available for research.
