  To date, top-performing optical flow estimation methods only take pairs of
consecutive frames into account. While elegant and appealing, the idea of using
more than two frames has not yet produced state-of-the-art results. We present
a simple, yet effective fusion approach for multi-frame optical flow that
benefits from longer-term temporal cues. Our method first warps the optical
flow from previous frames to the current, thereby yielding multiple plausible
estimates. It then fuses the complementary information carried by these
estimates into a new optical flow field. At the time of writing, our method
ranks first among published results in the MPI Sintel and KITTI 2015
benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.
