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In this work we propose a simple unsupervised approach for next frameprediction in video. Instead of directly predicting the pixels in a frame givenpast frames, we predict the transformations needed for generating the nextframe in a sequence, given the transformations of the past frames. This leadsto sharper results, while using a smaller prediction model. In order to enablea fair comparison between different video frame prediction models, we alsopropose a new evaluation protocol. We use generated frames as input to aclassifier trained with ground truth sequences. This criterion guarantees thatmodels scoring high are those producing sequences which preserve discriminativefeatures, as opposed to merely penalizing any deviation, plausible or not, fromthe ground truth. Our proposed approach compares favourably against moresophisticated ones on the UCF-101 data set, while also being more efficient interms of the number of parameters and computational cost.

Transformation-Based Models of Video Sequences