Abstract
Generating a novel image by manipulating two input images is an interesting research problem in the study of generative adversarial networks (GANs). We propose a new
GAN-based network that generates a fusion image with the
identity of input image x and the shape of input image y.
Our network can simultaneously train on more than two
image datasets in an unsupervised manner. We define an
identity loss LI to catch the identity of image x and a shape
loss LS to get the shape of y. In addition, we propose a
novel training method called Min-Patch training to focus
the generator on crucial parts of an image, rather than its
entirety. We show qualitative results on the VGG Youtube
Pose dataset, Eye dataset (MPIIGaze and UnityEyes), and
the Photo–Sketch–Cartoon dataset