  Classical computation of optical flow involves generic priors (regularizers)
that capture rudimentary statistics of images, but not long-range correlations
or semantics. On the other hand, fully supervised methods learn the regularity
in the annotated data, without explicit regularization and with the risk of
overfitting. We seek to learn richer priors on the set of possible flows that
are statistically compatible with an image. Once the prior is learned in a
supervised fashion, one can easily learn the full map to infer optical flow
directly from two or more images, without any need for (additional)
supervision. We introduce a novel architecture, called Conditional Prior
Network (CPN), and show how to train it to yield a conditional prior. When used
in conjunction with a simple optical flow architecture, the CPN beats all
variational methods and all unsupervised learning-based ones using the same
data term. It performs comparably to fully supervised ones, that however are
fine-tuned to a particular dataset. Our method, on the other hand, performs
well even when transferred between datasets.
