Functional Faces: Groupwise Dense Correspondence Using Functional Maps
Abstract
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps. The functional maps paradigm brings with it a number of advantages for face correspondence. First, it allows us to combine various notions of correspondence. We do so by proposing a number of face-specific functions, suited to either within- or between-subject correspondence. Second, we propose a groupwise variant of the method allowing us to compute cycle-consistent functional maps between all faces in a training set. Since functional maps are of much lower dimension than point-to-point correspondences, this is feasible even when the input meshes are very high resolution. Finally, we show how a functional map provides a geometric constraint that can be used to filter feature matches between non-rigidly deforming surfaces.
Cite
Text
Zhang et al. "Functional Faces: Groupwise Dense Correspondence Using Functional Maps." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.544Markdown
[Zhang et al. "Functional Faces: Groupwise Dense Correspondence Using Functional Maps." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/zhang2016cvpr-functional/) doi:10.1109/CVPR.2016.544BibTeX
@inproceedings{zhang2016cvpr-functional,
title = {{Functional Faces: Groupwise Dense Correspondence Using Functional Maps}},
author = {Zhang, Chao and Smith, William A. P. and Dessein, Arnaud and Pears, Nick and Dai, Hang},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2016},
doi = {10.1109/CVPR.2016.544},
url = {https://mlanthology.org/cvpr/2016/zhang2016cvpr-functional/}
}