  We present an unsupervised optical flow estimation method by proposing an
adaptive pyramid sampling in the deep pyramid network. Specifically, in the
pyramid downsampling, we propose an Content Aware Pooling (CAP) module, which
promotes local feature gathering by avoiding cross region pooling, so that the
learned features become more representative. In the pyramid upsampling, we
propose an Adaptive Flow Upsampling (AFU) module, where cross edge
interpolation can be avoided, producing sharp motion boundaries. Equipped with
these two modules, our method achieves the best performance for unsupervised
optical flow estimation on multiple leading benchmarks, including MPI-SIntel,
KITTI 2012 and KITTI 2015. Particuarlly, we achieve EPE=1.5 on KITTI 2012 and
F1=9.67% KITTI 2015, which outperform the previous state-of-the-art methods by
16.7% and 13.1%, respectively.
