Customized Automatic Face Beautification

Wang Chen, Peizhen Chen, Weijie Chen, Luojun Lin

Abstract: In the age of social media, posting attractive mugshots is commonplace, leading to an urgent need for automatic facial beautification techniques. To better meet the esthetic preferences of users, we devise a customized automatic face beautification task that can retouch the face adaptively to match the user-entered target score whilst preserving the ID information as much as possible. To accomplish this task, we propose a Human Esthetics Guided StyleGAN Inversion method to retouch each face in the embedding space using StyleGAN inversion. This process is guided by a pre-trained facial beauty prediction model that measures the difference between the target score and the predicted score of the retouched face. We conduct extensive experiments on various faces with different attributes, where the experimental results show that our method achieves the competitive performance, both in terms of visual effect and the proposed criterion.