Perceiving Systems, Computer Vision

Dynamic coupled component analysis

2001

Conference Paper

ps


We present a method for simultaneously learning linear models of multiple high dimensional data sets and the dependencies between them. For example, we learn asymmetrically coupled linear models for the faces of two different people and show how these models can be used to animate one face given a video sequence of the other. We pose the problem as a form of Asymmetric Coupled Component Analysis (ACCA) in which we simultaneously learn the subspaces for reducing the dimensionality of each dataset while coupling the parameters of the low dimensional representations. Additionally, a dynamic form of ACCA is proposed, that extends this work to model temporal dependencies in the data sets. To account for outliers and missing data, we formulate the problem in a statistically robust estimation framework. We review connections with previous work and illustrate the method with examples of synthesized dancing and the animation of facial avatars.

Author(s): De la Torre, F. and Black, M. J.
Book Title: IEEE Proc. Computer Vision and Pattern Recognition, CVPR’01
Volume: 2
Pages: 643-650
Year: 2001
Month: December
Publisher: IEEE

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Kauai, Hawaii

Links: pdf

BibTex

@inproceedings{Black:IEEE:2001,
  title = {Dynamic coupled component analysis},
  author = {De la Torre, F. and Black, M. J.},
  booktitle = {IEEE Proc. Computer Vision and Pattern Recognition, CVPR'01},
  volume = {2},
  pages = {643-650},
  publisher = {IEEE},
  address = {Kauai, Hawaii},
  month = dec,
  year = {2001},
  doi = {},
  month_numeric = {12}
}