  We present a self-supervised learning approach for optical flow. Our method
distills reliable flow estimations from non-occluded pixels, and uses these
predictions as ground truth to learn optical flow for hallucinated occlusions.
We further design a simple CNN to utilize temporal information from multiple
frames for better flow estimation. These two principles lead to an approach
that yields the best performance for unsupervised optical flow learning on the
challenging benchmarks including MPI Sintel, KITTI 2012 and 2015. More notably,
our self-supervised pre-trained model provides an excellent initialization for
supervised fine-tuning. Our fine-tuned models achieve state-of-the-art results
on all three datasets. At the time of writing, we achieve EPE=4.26 on the
Sintel benchmark, outperforming all submitted methods.
