  Current methods for image-to-image translation produce compelling results,
however, the applied transformation is difficult to control, since existing
mechanisms are often limited and non-intuitive. We propose ParGAN, a
generalization of the cycle-consistent GAN framework to learn image
transformations with simple and intuitive controls. The proposed generator
takes as input both an image and a parametrization of the transformation. We
train this network to preserve the content of the input image while ensuring
that the result is consistent with the given parametrization. Our approach does
not require paired data and can learn transformations across several tasks and
datasets. We show how, with disjoint image domains with no annotated
parametrization, our framework can create smooth interpolations as well as
learn multiple transformations simultaneously.
