A Log-likelihood Regularized KL Divergence for Video Prediction with A 3D Convolutional Variational Recurrent Network
This work provides an incremental improvement in video prediction accuracy for researchers and practitioners working with sequential data modeling.
This paper introduces a new variational model for video frame prediction that incorporates 3D convolutions and a log-likelihood regularized KL divergence. The model achieves superior performance on several benchmarks while using fewer parameters compared to existing methods.
The use of latent variable models has shown to be a powerful tool for modeling probability distributions over sequences. In this paper, we introduce a new variational model that extends the recurrent network in two ways for the task of video frame prediction. First, we introduce 3D convolutions inside all modules including the recurrent model for future frame prediction, inputting and outputting a sequence of video frames at each timestep. This enables us to better exploit spatiotemporal information inside the variational recurrent model, allowing us to generate high-quality predictions. Second, we enhance the latent loss of the variational model by introducing a maximum likelihood estimate in addition to the KL divergence that is commonly used in variational models. This simple extension acts as a stronger regularizer in the variational autoencoder loss function and lets us obtain better results and generalizability. Experiments show that our model outperforms existing video prediction methods on several benchmarks while requiring fewer parameters.