  This work advocates Eulerian motion representation learning over the current
standard Lagrangian optical flow model. Eulerian motion is well captured by
using phase, as obtained by decomposing the image through a complex-steerable
pyramid. We discuss the gain of Eulerian motion in a set of practical use
cases: (i) action recognition, (ii) motion prediction in static images, (iii)
motion transfer in static images and, (iv) motion transfer in video. For each
task we motivate the phase-based direction and provide a possible approach.
