  We present a framework to use recently introduced Capsule Networks for
solving the problem of Optical Flow, one of the fundamental computer vision
tasks. Most of the existing state of the art deep architectures either uses a
correlation oepration to match features from them. While correlation layer is
sensitive to the choice of hyperparameters and does not put a prior on the
underlying structure of the object, spatio temporal features will be limited by
the network's receptive field. Also, we as humans look at moving objects as
whole, something which cannot be encoded by correlation or spatio temporal
features. Capsules, on the other hand, are specialized to model seperate
entities and their pose as a continuous matrix. Thus, we show that a simpler
linear operation over poses of the objects detected by the capsules in enough
to model flow. We show reslts on a small toy dataset where we outperform
FlowNetC and PWC-Net models.
