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