Face Style Transfer and Removal with Generative Adversarial Network
Qiang Zhu, Ze-Nian Li · 2019
In this paper, we present a method to transfer the style of a stylized face to another face without style and recover photo-realistic face from the same stylized face image simultaneously. Here style refers to the local patterns or textures of some existing paintings. Style transfer gives a new way for artistic creation while style removal can be beneficial for face verification or photo-realistic content editing. Our approach contains two components: the Style Transfer Network (STN) and the Style Removal Network (SRN). STN renders the style of the stylized image to the non-stylized image and the SRN is designed to remove the style of a stylized photo. By applying the two networks successively to an original input photo, the output should match the input photo. The experiment results in a variety of portraits and styles demonstrate our approach's effectiveness.