Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis
Abstract
We describe a framework for real-time tracking of facial expressions that uses neurally-inspired correlation and interpolation methods. A distributed view-based representation is used to characterize facial state, and is computed using a replicated correlation network. The ensemble response of the set of view correlation scores is input to a network based interpolation method, which maps perceptual state to motor control states for a simulated 3-D face model. Activation levels of the motor state correspond to muscle activations in an anatomically derived model. By integrating fast and robust 2-D processing with 3-D models, we obtain a system that is able to quickly track and interpret complex facial motions in real-time.
Cite
Text
Darrell et al. "Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis." Neural Information Processing Systems, 1994.Markdown
[Darrell et al. "Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/darrell1994neurips-correlation/)BibTeX
@inproceedings{darrell1994neurips-correlation,
title = {{Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis}},
author = {Darrell, Trevor and Essa, Irfan A. and Pentland, Alex},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {909-916},
url = {https://mlanthology.org/neurips/1994/darrell1994neurips-correlation/}
}