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.

利用大量未标记的视频来学习场景动态的模型，提出了一种具有时空卷积结构的生成对抗网络来生成视频，该模型能够更好地预测静态图像的合理未来，并且能够识别动作的有用特征，这表明场景动态是表示学习的一个有前途的信号。