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Face style transfer and removal with generative adversarial network

Resource type
Thesis type
(Thesis) M.Sc.
Date created
Author: Zhu, Qiang
Style transfer plays a vital role in image manipulation and creates new artistic works in different artistic styles from existing photographs. While style transfer has been widely studied, recovering photo-realistic images from corresponding artistic works has not been fully investigated. And all previous work considers style transfer and removal as separate problems. In this thesis, we present a method to transfer the style of a stylized face to a different 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 the stylized images. Style transfer gives a new way for artistic creation while style removal can be beneficial for face verification, photo-realistic content editing or facial analysis. 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.
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Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Li, Ze-Nian
Member of collection
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