  Recently, convolutional networks (convnets) have proven useful for predicting
optical flow. Much of this success is predicated on the availability of large
datasets that require expensive and involved data acquisition and laborious la-
beling. To bypass these challenges, we propose an unsuper- vised approach
(i.e., without leveraging groundtruth flow) to train a convnet end-to-end for
predicting optical flow be- tween two images. We use a loss function that
combines a data term that measures photometric constancy over time with a
spatial term that models the expected variation of flow across the image.
Together these losses form a proxy measure for losses based on the groundtruth
flow. Empiri- cally, we show that a strong convnet baseline trained with the
proposed unsupervised approach outperforms the same network trained with
supervision on the KITTI dataset.
