  We present an unsupervised learning framework for simultaneously training
single-view depth prediction and optical flow estimation models using unlabeled
video sequences. Existing unsupervised methods often exploit brightness
constancy and spatial smoothness priors to train depth or flow models. In this
paper, we propose to leverage geometric consistency as additional supervisory
signals. Our core idea is that for rigid regions we can use the predicted scene
depth and camera motion to synthesize 2D optical flow by backprojecting the
induced 3D scene flow. The discrepancy between the rigid flow (from depth
prediction and camera motion) and the estimated flow (from optical flow model)
allows us to impose a cross-task consistency loss. While all the networks are
jointly optimized during training, they can be applied independently at test
time. Extensive experiments demonstrate that our depth and flow models compare
favorably with state-of-the-art unsupervised methods.
