  Recently, a surge of face editing techniques have been proposed to employ the
pretrained StyleGAN for semantic manipulation. To successfully edit a real
image, one must first convert the input image into StyleGAN's latent variables.
However, it is still challenging to find latent variables, which have the
capacity for preserving the appearance of the input subject (e.g., identity,
lighting, hairstyles) as well as enabling meaningful manipulations. In this
paper, we present a method to expand the latent space of StyleGAN with
additional content features to break down the trade-off between low-distortion
and high-editability. Specifically, we proposed a two-branch model, where the
style branch first tackles the entanglement issue by the sparse manipulation of
latent codes, and the content branch then mitigates the distortion issue by
leveraging the content and appearance details from the input image. We confirm
the effectiveness of our method using extensive qualitative and quantitative
experiments on real face editing and reconstruction tasks.
