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The use of latent variable models has shown to be a powerful tool formodeling probability distributions over sequences. In this paper, we introducea new variational model that extends the recurrent network in two ways for thetask of video frame prediction. First, we introduce 3D convolutions inside allmodules including the recurrent model for future frame prediction, inputtingand outputting a sequence of video frames at each timestep. This enables us tobetter exploit spatiotemporal information inside the variational recurrentmodel, allowing us to generate high-quality predictions. Second, we enhance thelatent loss of the variational model by introducing a maximum likelihoodestimate in addition to the KL divergence that is commonly used in variationalmodels. This simple extension acts as a stronger regularizer in the variationalautoencoder loss function and lets us obtain better results andgeneralizability. Experiments show that our model outperforms existing videoprediction methods on several benchmarks while requiring fewer parameters.

A Log-likelihood Regularized KL Divergence for Video Prediction with A 3D Convolutional Variational Recurrent Network