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Recent breakthroughs in adversarial generative modeling have led to modelscapable of producing video samples of high quality, even on large and complexdatasets of real-world video. In this work, we focus on the task of videoprediction, where given a sequence of frames extracted from a video, the goalis to generate a plausible future sequence. We first improve the state of theart by performing a systematic empirical study of discriminator decompositionsand proposing an architecture that yields faster convergence and higherperformance than previous approaches. We then analyze recurrent units in thegenerator, and propose a novel recurrent unit which transforms its past hiddenstate according to predicted motion-like features, and refines it to handledis-occlusions, scene changes and other complex behavior. We show that thisrecurrent unit consistently outperforms previous designs. Our final model leadsto a leap in the state-of-the-art performance, obtaining a test set FrechetVideo Distance of 25.7, down from 69.2, on the large-scale Kinetics-600dataset.

Transformation-based Adversarial Video Prediction on Large-Scale Data