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.​


Presented at:
Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 18-22, 2018
Year:
Jun 18 2018
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 Record created 2018-04-20, last modified 2019-03-17

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