Increasing the density of active appearance models
Abstract
Active Appearance Models (AAMs) typically only use
50-100 mesh vertices because they are usually constructed
from a set of training images with the vertices hand-labeled
on them. In this paper, we propose an algorithm to increase
the density of an AAM. Our algorithm operates by iteratively
building the AAM, refitting the AAM to the training
data, and refining the AAM.We compare our algorithm with
the state of the art in optical flow algorithms and find it to be
significantly more accurate. We also show that dense AAMs
can be fit more robustly than sparse ones. Finally, we show
how our algorithm can be used to construct AAMs automatically,
starting with a single affine model that is subsequently
refined to model non-planarity and non-rigidity.
Cite
@inproceedings{RamnathBMR_CVPR_2008,
author = {Kannan Ramnath and Simon Baker and Ian Matthews and Deva Ramanan},
title = {Increasing the density of active appearance models},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2008},
doi = {10.1109/cvpr.2008.4587517},
url = {https://doi.org/10.1109/cvpr.2008.4587517},
}