arXiv:1704.05693cs.CV
Unsupervised Creation of Parameterized Avatars
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
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.
I spoke in hyperbole, there were dozens of papers using generative networks to create real looking simulated data which is the more common approach. You start with noise + some features you want in your image, then you turn that into a realistic image. The notable exception is…
alexbeloiView on HNParent context