  In this paper, we investigate an open research task of generating 3D cartoon
face shapes from single 2D GAN generated human faces and without 3D
supervision, where we can also manipulate the facial expressions of the 3D
shapes. To this end, we discover the semantic meanings of StyleGAN latent
space, such that we are able to produce face images of various expressions,
poses, and lighting by controlling the latent codes. Specifically, we first
finetune the pretrained StyleGAN face model on the cartoon datasets. By feeding
the same latent codes to face and cartoon generation models, we aim to realize
the translation from 2D human face images to cartoon styled avatars. We then
discover semantic directions of the GAN latent space, in an attempt to change
the facial expressions while preserving the original identity. As we do not
have any 3D annotations for cartoon faces, we manipulate the latent codes to
generate images with different poses and lighting, such that we can reconstruct
the 3D cartoon face shapes. We validate the efficacy of our method on three
cartoon datasets qualitatively and quantitatively.
