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The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three majordoi:10.1109/cvpr.2017.179 dblp:conf/cvpr/IlgMSKDB17 fatcat:ffxpoiw67vaufazauyniwnyzqi