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Learning to predict future images from a video sequence involves theconstruction of an internal representation that models the image evolutionaccurately, and therefore, to some degree, its content and dynamics. This iswhy pixel-space video prediction may be viewed as a promising avenue forunsupervised feature learning. In addition, while optical flow has been a verystudied problem in computer vision for a long time, future frame prediction israrely approached. Still, many vision applications could benefit from theknowledge of the next frames of videos, that does not require the complexity oftracking every pixel trajectories. In this work, we train a convolutionalnetwork to generate future frames given an input sequence. To deal with theinherently blurry predictions obtained from the standard Mean Squared Error(MSE) loss function, we propose three different and complementary featurelearning strategies: a multi-scale architecture, an adversarial trainingmethod, and an image gradient difference loss function. We compare ourpredictions to different published results based on recurrent neural networkson the UCF101 dataset

Deep multi-scale video prediction beyond mean square error