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We present a framework to use recently introduced Capsule Networks forsolving the problem of Optical Flow, one of the fundamental computer visiontasks. Most of the existing state of the art deep architectures either uses acorrelation oepration to match features from them. While correlation layer issensitive to the choice of hyperparameters and does not put a prior on theunderlying structure of the object, spatio temporal features will be limited bythe network's receptive field. Also, we as humans look at moving objects aswhole, something which cannot be encoded by correlation or spatio temporalfeatures. Capsules, on the other hand, are specialized to model seperateentities and their pose as a continuous matrix. Thus, we show that a simplerlinear operation over poses of the objects detected by the capsules in enoughto model flow. We show reslts on a small toy dataset where we outperformFlowNetC and PWC-Net models.

CapsFlow: Optical Flow Estimation with Capsule Networks