  Generative adversarial networks (GANs) have been successfully applied to
transfer visual attributes in many domains, including that of human face
images. This success is partly attributable to the facts that human faces have
similar shapes and the positions of eyes, noses, and mouths are fixed among
different people. Attribute transfer is more challenging when the source and
target domain share different shapes. In this paper, we introduce a shape-aware
GAN model that is able to preserve shape when transferring attributes, and
propose its application to some real-world domains. Compared to other
state-of-art GANs-based image-to-image translation models, the model we propose
is able to generate more visually appealing results while maintaining the
quality of results from transfer learning.
