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Temporal coherence is a valuable source of information in the context ofoptical flow estimation. However, finding a suitable motion model to leveragethis information is a non-trivial task. In this paper we propose anunsupervised online learning approach based on a convolutional neural network(CNN) that estimates such a motion model individually for each frame. Byrelating forward and backward motion these learned models not only allow toinfer valuable motion information based on the backward flow, they also help toimprove the performance at occlusions, where a reliable prediction isparticularly useful. Moreover, our learned models are spatially variant andhence allow to estimate non-rigid motion per construction. This, in turns,allows to overcome the major limitation of recent rigidity-based approachesthat seek to improve the estimation by incorporating additional stereo/SfMconstraints. Experiments demonstrate the usefulness of our new approach. Theynot only show a consistent improvement of up to 27% for all major benchmarks(KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction,they also show top results for the MPI Sintel benchmark -- the one of the threebenchmarks that contains the largest amount of non-rigid motion.

ProFlow: Learning to Predict Optical Flow