  This paper deals with the scarcity of data for training optical flow
networks, highlighting the limitations of existing sources such as labeled
synthetic datasets or unlabeled real videos. Specifically, we introduce a
framework to generate accurate ground-truth optical flow annotations quickly
and in large amounts from any readily available single real picture. Given an
image, we use an off-the-shelf monocular depth estimation network to build a
plausible point cloud for the observed scene. Then, we virtually move the
camera in the reconstructed environment with known motion vectors and rotation
angles, allowing us to synthesize both a novel view and the corresponding
optical flow field connecting each pixel in the input image to the one in the
new frame. When trained with our data, state-of-the-art optical flow networks
achieve superior generalization to unseen real data compared to the same models
trained either on annotated synthetic datasets or unlabeled videos, and better
specialization if combined with synthetic images.
