  We present an unsupervised learning approach for optical flow estimation by
improving the upsampling and learning of pyramid network. We design a
self-guided upsample module to tackle the interpolation blur problem caused by
bilinear upsampling between pyramid levels. Moreover, we propose a pyramid
distillation loss to add supervision for intermediate levels via distilling the
finest flow as pseudo labels. By integrating these two components together, our
method achieves the best performance for unsupervised optical flow learning on
multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
In particular, we achieve EPE=1.4 on KITTI 2012 and F1=9.38% on KITTI 2015,
which outperform the previous state-of-the-art methods by 22.2% and 15.7%,
respectively.
