High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex network architectures and highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: maximizing the capacity of a standard convolutional neural network. We perform the first large-scale empirical study of the effect of capacity on video prediction models. In our experiments, we demonstrate our results on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling first-person car driving.