Revisiting Hierarchical Approach for Persistent Long-Term Video PredictionDownload PDF

Sep 28, 2020 (edited Mar 18, 2021)ICLR 2021 PosterReaders: Everyone
  • Keywords: Video prediction, generative model, long-term prediction
  • Abstract: Learning to predict the long-term future of video frames is notoriously challenging due to the inherent ambiguities in a distant future and dramatic amplification of prediction error over time. Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less than a few seconds), while extrapolating it to a longer future quickly leads to destruction in structure and content. In this work, we revisit the hierarchical models in video prediction. Our method generates future frames by first estimating a sequence of dense semantic structures and subsequently translating the estimated structures to pixels by video-to-video translation model. Despite the simplicity, we show that modeling structures and their dynamics in categorical structure space with stochastic sequential estimator leads to surprisingly successful long-term prediction. We evaluate our method on two challenging video prediction scenarios, \emph{car driving} and \emph{human dancing}, and demonstrate that it can generate complicated scene structures and motions over a very long time horizon (\ie~thousands frames), setting a new standard of video prediction with orders of magnitude longer prediction time than existing approaches. Video results are available at https://1konny.github.io/HVP/.
  • One-sentence Summary: We propose a simple yet effective hierarchical video prediction model that can synthesize future frames orders of magnitude longer than existing methods (thousands frames)
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