  We address unsupervised optical flow estimation for ego-centric motion. We
argue that optical flow can be cast as a geometrical warping between two
successive video frames and devise a deep architecture to estimate such
transformation in two stages. First, a dense pixel-level flow is computed with
a geometric prior imposing strong spatial constraints. Such prior is typical of
driving scenes, where the point of view is coherent with the vehicle motion. We
show how such global transformation can be approximated with an homography and
how spatial transformer layers can be employed to compute the flow field
implied by such transformation. The second stage then refines the prediction
feeding a second deeper network. A final reconstruction loss compares the
warping of frame X(t) with the subsequent frame X(t+1) and guides both
estimates. The model, which we named TransFlow, performs favorably compared to
other unsupervised algorithms, and shows better generalization compared to
supervised methods with a 3x reduction in error on unseen data.
