  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.
