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.

提出了一种新的基于生成敌对网络 (GANs) 的网络，可以在无监督的情况下同时训练两个或两个以上的图像数据集，并利用定义的相同损失 LI 和形状损失 LS ，以及一种名为 Min-Patch 训练的新训练方法，使其可以生成一个拥有输入图像 x 的身份和输入图像 y 的形状的融合图像。在 VGG Youtube 姿势数据集、Eye 数据集 (MPIIGaze 和 UnityEyes) 和 Photo-Sketch-Cartoon 数据集上展示了定性结果。