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arXiv · 1903.01434

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Manoj Kumar·Mohammad Babaeizadeh·Dumitru Erhan·Chelsea Finn·Sergey Levine·Laurent Dinh·Durk Kingma
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Abstract

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures. Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally as in the case of pixel-level autoregressive models, or do not directly optimize the likelihood of the data. To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions. We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modelling of video.

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BibTeXRIS

Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma. 2019-03-04. VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. https://arxiv.org/abs/1903.01434

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