  Although significant progress has been made in synthesizing high-quality and
visually realistic face images by unconditional Generative Adversarial Networks
(GANs), there still lacks of control over the generation process in order to
achieve semantic face editing. In addition, it remains very challenging to
maintain other face information untouched while editing the target attributes.
In this paper, we propose a novel learning framework, called GuidedStyle, to
achieve semantic face editing on StyleGAN by guiding the image generation
process with a knowledge network. Furthermore, we allow an attention mechanism
in StyleGAN generator to adaptively select a single layer for style
manipulation. As a result, our method is able to perform disentangled and
controllable edits along various attributes, including smiling, eyeglasses,
gender, mustache and hair color. Both qualitative and quantitative results
demonstrate the superiority of our method over other competing methods for
semantic face editing. Moreover, we show that our model can be also applied to
different types of real and artistic face editing, demonstrating strong
generalization ability.
