  Classical approaches for estimating optical flow have achieved rapid progress
in the last decade. However, most of them are too slow to be applied in
real-time video analysis. Due to the great success of deep learning, recent
work has focused on using CNNs to solve such dense prediction problems. In this
paper, we investigate a new deep architecture, Densely Connected Convolutional
Networks (DenseNet), to learn optical flow. This specific architecture is ideal
for the problem at hand as it provides shortcut connections throughout the
network, which leads to implicit deep supervision. We extend current DenseNet
to a fully convolutional network to learn motion estimation in an unsupervised
manner. Evaluation results on three standard benchmarks demonstrate that
DenseNet is a better fit than other widely adopted CNN architectures for
optical flow estimation.
