FAEC-GAN: An unsupervised face-to-anime translation based on edge enhancement and coordinate attention

Animation is a widely loved artistic form with high abstraction and powerful expression. The task of image translation from face to anime involves complex geometric and texture transformations, and requires the generated images with clear lines. The existing unsupervised image translation frameworks are often ineffective for this task. According to the characteristics of animation image, we propose an animation translation method based on edge enhancement and coordinate attention, which is called FAEC-GAN. We design a novel edge discrimination network to identify the edge features of images, so that the generated anime images can present clear and coherent lines. And the coordinate attention module is introduced in the encoder to adapt the model to the geometric changes in translation, so as to produce more realistic animation images. In addition, our method combines the focal frequency loss and pixel loss, which can pay attention to both the frequency domain information and pixel information of the generated image to improve the visual effect of the image. The experimental results demonstrate that FAEC-GAN is superior to the state-of-the-art methods in the task of face-to-animation image translation.