Modeling Facial Geometry Using Compositional VAEs

Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3877-3886

Abstract


We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more local deformations. Based on that, we propose a new parameterization of facial geometry that naturally decomposes the structure of the human face into a set of semantically meaningful levels of detail. This parameterization enables us to do model fitting while capturing varying level of detail under different types of geometrical constraints.

Related Material


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[bibtex]
@InProceedings{Bagautdinov_2018_CVPR,
author = {Bagautdinov, Timur and Wu, Chenglei and Saragih, Jason and Fua, Pascal and Sheikh, Yaser},
title = {Modeling Facial Geometry Using Compositional VAEs},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}