  Akin to many subareas of computer vision, the recent advances in deep
learning have also significantly influenced the literature on optical flow.
Previously, the literature had been dominated by classical energy-based models,
which formulate optical flow estimation as an energy minimization problem.
However, as the practical benefits of Convolutional Neural Networks (CNNs) over
conventional methods have become apparent in numerous areas of computer vision
and beyond, they have also seen increased adoption in the context of motion
estimation to the point where the current state of the art in terms of accuracy
is set by CNN approaches. We first review this transition as well as the
developments from early work to the current state of CNNs for optical flow
estimation. Alongside, we discuss some of their technical details and compare
them to recapitulate which technical contribution led to the most significant
accuracy improvements. Then we provide an overview of the various optical flow
approaches introduced in the deep learning age, including those based on
alternative learning paradigms (e.g., unsupervised and semi-supervised methods)
as well as the extension to the multi-frame case, which is able to yield
further accuracy improvements.
