Unsupervised Creation of Parameterized Avatars
This addresses the need for automated avatar creation, but it appears incremental as it extends existing domain adaptation and transfer problems.
The paper tackles the problem of mapping an input image to a parameterized avatar without supervision, achieving results that automatically create avatars as demonstrated experimentally.
We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping's objective is to have the output image as similar as possible to the input image. During training, no supervision is given in the form of matching inputs and outputs. This learning problem extends two literature problems: unsupervised domain adaptation and cross domain transfer. We define a generalization bound that is based on discrepancy, and employ a GAN to implement a network solution that corresponds to this bound. Experimentally, our method is shown to solve the problem of automatically creating avatars.