  Recently, synthesizing personalized characters from a single user-given
portrait has received remarkable attention as a drastic popularization of
social media and the metaverse. The input image is not always in frontal view,
thus it is important to acquire or predict canonical view for 3D modeling or
other applications. Although the progress of generative models enables the
stylization of a portrait, obtaining the stylized image in canonical view is
still a challenging task. There have been several studies on face
frontalization but their performance significantly decreases when input is not
in the real image domain, e.g., cartoon or painting. Stylizing after
frontalization also results in degenerated output. In this paper, we propose a
novel and unified framework which generates stylized portraits in canonical
view. With a proposed latent mapper, we analyze and discover frontalization
mapping in a latent space of StyleGAN to stylize and frontalize at once. In
addition, our model can be trained with unlabelled 2D image sets, without any
3D supervision. The effectiveness of our method is demonstrated by experimental
results.
