Bagautdinov, TimurWu, ChengleiSaragih, JasonSheikh, YaserFua, Pascal2018-04-202018-04-202018-04-202018-06-1810.1109/CVPR.2018.00408https://infoscience.epfl.ch/handle/20.500.14299/146094WOS:000457843604003We 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.​computer visionface modelingdeep learningvariational methodstext::conference output::conference proceedings::conference paper