The paper "Generating Videos with Scene Dynamics" by Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba explores the use of large amounts of unlabeled video data to learn a model of scene dynamics for both video recognition and generation tasks. The authors propose a generative adversarial network (GAN) with a spatio-temporal convolutional architecture that can distinguish between the foreground and background of a scene, enabling more realistic video generation. Experiments show that the model can generate short videos with plausible dynamics and motions, outperforming simple baselines. The model also demonstrates utility in predicting plausible futures of static images and learning useful features for action classification with minimal supervision. The paper highlights the potential of generative video models in various applications, including simulations, forecasting, and representation learning. The authors discuss related work, present the generative model, and detail experiments on video generation, representation learning, and future prediction, concluding that learning from unlabeled video data is a promising approach for understanding scene dynamics.The paper "Generating Videos with Scene Dynamics" by Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba explores the use of large amounts of unlabeled video data to learn a model of scene dynamics for both video recognition and generation tasks. The authors propose a generative adversarial network (GAN) with a spatio-temporal convolutional architecture that can distinguish between the foreground and background of a scene, enabling more realistic video generation. Experiments show that the model can generate short videos with plausible dynamics and motions, outperforming simple baselines. The model also demonstrates utility in predicting plausible futures of static images and learning useful features for action classification with minimal supervision. The paper highlights the potential of generative video models in various applications, including simulations, forecasting, and representation learning. The authors discuss related work, present the generative model, and detail experiments on video generation, representation learning, and future prediction, concluding that learning from unlabeled video data is a promising approach for understanding scene dynamics.