Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines
Abstract
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.
Cite
Text
Zeiler et al. "Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines." Neural Information Processing Systems, 2011.Markdown
[Zeiler et al. "Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines." Neural Information Processing Systems, 2011.](https://mlanthology.org/neurips/2011/zeiler2011neurips-facial/)BibTeX
@inproceedings{zeiler2011neurips-facial,
title = {{Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines}},
author = {Zeiler, Matthew D. and Taylor, Graham W. and Sigal, Leonid and Matthews, Iain and Fergus, Rob},
booktitle = {Neural Information Processing Systems},
year = {2011},
pages = {1629-1637},
url = {https://mlanthology.org/neurips/2011/zeiler2011neurips-facial/}
}