Exploiting the power of StarGANv2 in the wild

Abstract

With wide-spread usage of style transfer, numerous methods for style transfer draw an increasing attention. Several methods to enhance the efficiency of style transformers have been made, one of them is StarGANv2, a method for multiple-style transfer, which can transform a batch of source pictures into other pictures with different styles. The main difference of StarGANv2 with other style transformers is that it uses style code to represent the styles to enable StarGANv2 to complete multiple-style transformation. The authors of StarGANv2 use CelebA-HQ and AFHQ dataset to train the model and test the model, and the results are pretty better than other style transformers. The goal of this paper is to exploit the effectiveness of StarGANv2 in the real-world scenes, such as over exposure or the angle facing the camera. The results validate the power of StarGANv2 where the model is robust enough to transfer the pictures into other styles. To achieve this, the authors of StarGANv2 use the photo clipped in videos which record real-world animals and form a new dataset. Then, the authors of StarGANv2 use the dataset to test the pre-trained model which is trained by AFHQ dataset and evaluate it according to FID metric. The authors of StarGANv2 draw a conclusion that StarGANv2 is robust in real world scenes. The meaning of this paper is that the authors get the real-world usage of StarGANv2 and have a test of StarGANv2’s robustness in real world photos and validate the potential of StarGANv2 in real-world applications.