Abstract
Deep generative models learned through adversarial
training have become increasingly popular for their ability to generate naturalistic image textures. However, aside
from their texture, the visual appearance of objects is significantly influenced by their shape geometry; information
which is not taken into account by existing generative models. This paper introduces the Geometry-Aware Generative
Adversarial Networks (GAGAN) for incorporating geometric information into the image generation process. Specifically, in GAGAN the generator samples latent variables
from the probability space of a statistical shape model. By
mapping the output of the generator to a canonical coordinate frame through a differentiable geometric transformation, we enforce the geometry of the objects and add an
implicit connection from the prior to the generated object.
Experimental results on face generation indicate that the
GAGAN can generate realistic images of faces with arbitrary facial attributes such as facial expression, pose, and
morphology, that are of better quality than current GANbased methods. Our method can be used to augment any
existing GAN architecture and improve the quality of the
images generated.