Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)
Matthew Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
We present a type of Temporal Restricted Boltzmann Machine that defines a probability distribution over an output sequence conditional on an input sequence. It shares the desirable properties of RBMs: efficient exact inference, an exponentially more expressive latent state than HMMs, and the ability to model nonlinear structure and dynamics. We apply our model to a challenging real-world graphics problem: facial expression transfer. Our results demonstrate improved performance over several baselines modeling high-dimensional 2D and 3D data.