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Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines
Matthew D Zeiler · Graham Taylor · Leonid Sigal · Iain Matthews · Rob Fergus

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

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.

Author Information

Matthew D Zeiler (NYU / Clarifai)
Graham Taylor (University of Guelph / Vector Institute)
Leonid Sigal (University of British Columbia)
Iain Matthews (Disney Research)
Rob Fergus (DeepMind / NYU)

Rob Fergus is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He received a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford in 2005. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman. He has received several awards including a CVPR best paper prize, a Sloan Fellowship & NSF Career award and the IEEE Longuet-Higgins prize.

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