Vondrick, CarlPirsiavash, HamedTorralba, Antonio2019-06-272019-06-272016Carl Vondrick, et.al, Generating Videos with Scene Dynamics, NIPS 2016, http://papers.nips.cc/paper/6194-generating-videos-with-scene-dynamicshttp://hdl.handle.net/11603/1431530th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain.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.9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.unlabeled videovideo recognition taskvideo generation taskgenerative video modelsText