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Poster
Generating Videos with Scene Dynamics
Carl Vondrick · Hamed Pirsiavash · Antonio Torralba

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #29
in Wednesday Posters »

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.

Keywords: (Other) Unsupervised Learning Methods · (Application) Computer Vision · Large Scale Learning and Big Data
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Author Information

Carl Vondrick (MIT)
Hamed Pirsiavash (University of Maryland, Baltimore County)
Antonio Torralba (MIT)

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