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The estimation of optical flow is an ambiguous task due to the lack ofcorrespondence at occlusions, shadows, reflections, lack of texture and changesin illumination over time. Thus, unsupervised methods face major challenges asthey need to tune complex cost functions with several terms designed to handleeach of these sources of ambiguity. In contrast, supervised methods avoid thesechallenges altogether by relying on explicit ground truth optical flow obtaineddirectly from synthetic or real data. In the case of synthetic data, the groundtruth provides an exact and explicit description of what optical flow to assignto a given scene. However, the domain gap between synthetic data and real dataoften limits the ability of a trained network to generalize. In the case ofreal data, the ground truth is obtained through multiple sensors and additionaldata processing, which might introduce persistent errors and contaminate it. Asa solution to these issues, we introduce a novel method to build a training setof pseudo-real images that can be used to train optical flow in a supervisedmanner. Our dataset uses two unpaired frames from real data and creates pairsof frames by simulating random warps, occlusions with super-pixels, shadows andillumination changes, and associates them to their corresponding exact opticalflow. We thus obtain the benefit of directly training on real data while havingaccess to an exact ground truth. Training with our datasets on the Sintel andKITTI benchmarks is straightforward and yields models on par or with state ofthe art performance compared to much more sophisticated training approaches.

Optical Flow Dataset Synthesis from Unpaired Images