Modeling Facial Geometry using Compositional VAEs
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.​
bagautdinov.cvae.pdf
Postprint
openaccess
6.77 MB
Adobe PDF
0cc77490c12309384098381b35a1867f
supp.zip
openaccess
14.26 MB
ZIP
388163ece9e6cd6c37fe1913969f9840