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Modeling Facial Geometry Using Compositional VAEs

Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh
CVPR 2018
doi:10.1109/CVPR.2018.00408 /cvpr/2018/bagautdinov2018cvpr-modeling/

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.

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Text

Bagautdinov et al. "Modeling Facial Geometry Using Compositional VAEs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00408

Markdown

[Bagautdinov et al. "Modeling Facial Geometry Using Compositional VAEs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/bagautdinov2018cvpr-modeling/) doi:10.1109/CVPR.2018.00408

BibTeX

@inproceedings{bagautdinov2018cvpr-modeling,
  title     = {{Modeling Facial Geometry Using Compositional VAEs}},
  author    = {Bagautdinov, Timur and Wu, Chenglei and Saragih, Jason and Fua, Pascal and Sheikh, Yaser},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00408},
  url       = {https://mlanthology.org/cvpr/2018/bagautdinov2018cvpr-modeling/}
}