  We present a new lightweight CNN-based algorithm for multi-frame optical flow
estimation. Our solution introduces a double recurrence over spatial scale and
time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell.
It includes (i) a temporal recurrence based on conveying learned features
rather than optical flow estimates; (ii) an occlusion detection process which
is coupled with optical flow estimation and therefore uses a very limited
number of extra parameters. The resulting STaRFlow algorithm gives
state-of-the-art performances on MPI Sintel and Kitti2015 and involves
significantly less parameters than all other methods with comparable results.
