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High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Ruben Villegas · Arkanath Pathak · Harini Kannan · Dumitru Erhan · Quoc V Le · Honglak Lee

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #148

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 inductive biases inside network architectures with 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: finding minimal inductive bias for video prediction while maximizing network capacity. We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.

Author Information

Ruben Villegas (Adobe Research / U. Michigan)
Arkanath Pathak (Google)
Harini Kannan (Google Brain)
Dumitru Erhan (Google Brain)
Quoc V Le (Google)
Honglak Lee (Google / U. Michigan)

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