Face transformation using StyleGAN - Cui, Naichuan (Nanyang Technological University, 2021)
With the development of the application of computer vision technology, face editing applications become common in various scenarios, and they are widely used in areas such as image beautification, live video streaming, and face confrontation attacks. In recent years, with the emergence of generative adversarial networks, the quality of face image generation has notably improved, making face editing methods even more popular. The proposal of StyleGAN in 2018 has led to great progress in the resolution and quality of face generation, and face-related research using StyleGAN has become a hot topic. The editing of a real facial image requires first obtaining its latent code, then using the vector of corresponding attributes for editing, and finally converting the edited latent code into a face image. In this dissertation, the whole process of face transformation using StyleGAN is investigated, and some improvements are made to the existing methods. The main work of this dissertation is as follows: