  We investigate two crucial and closely related aspects of CNNs for optical
flow estimation: models and training. First, we design a compact but effective
CNN model, called PWC-Net, according to simple and well-established principles:
pyramidal processing, warping, and cost volume processing. PWC-Net is 17 times
smaller in size, 2 times faster in inference, and 11\% more accurate on Sintel
final than the recent FlowNet2 model. It is the winning entry in the optical
flow competition of the robust vision challenge. Next, we experimentally
analyze the sources of our performance gains. In particular, we use the same
training procedure of PWC-Net to retrain FlowNetC, a sub-network of FlowNet2.
The retrained FlowNetC is 56\% more accurate on Sintel final than the
previously trained one and even 5\% more accurate than the FlowNet2 model. We
further improve the training procedure and increase the accuracy of PWC-Net on
Sintel by 10\% and on KITTI 2012 and 2015 by 20\%. Our newly trained model
parameters and training protocols will be available on
https://github.com/NVlabs/PWC-Net
