资源论文Modeling Facial Geometry using Compositional VAEs

Modeling Facial Geometry using Compositional VAEs

2019-10-14 | |  59 |   37 |   0
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

上一篇:Monocular Relative Depth Perception with Web Stereo Data Supervision

下一篇:Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

用户评价
全部评价

热门资源

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • dynamical system ...

    allows to preform manipulations of heavy or bul...

  • The Variational S...

    Unlike traditional images which do not offer in...