  Recent advances in generative visual models and neural radiance fields have
greatly boosted 3D-aware image synthesis and stylization tasks. However,
previous NeRF-based work is limited to single scene stylization, training a
model to generate 3D-aware cartoon faces with arbitrary styles remains
unsolved. We propose ArtNeRF, a novel face stylization framework derived from
3D-aware GAN to tackle this problem. In this framework, we utilize an
expressive generator to synthesize stylized faces and a triple-branch
discriminator module to improve the visual quality and style consistency of the
generated faces. Specifically, a style encoder based on contrastive learning is
leveraged to extract robust low-dimensional embeddings of style images,
empowering the generator with the knowledge of various styles. To smooth the
training process of cross-domain transfer learning, we propose an adaptive
style blending module which helps inject style information and allows users to
freely tune the level of stylization. We further introduce a neural rendering
module to achieve efficient real-time rendering of images with higher
resolutions. Extensive experiments demonstrate that ArtNeRF is versatile in
generating high-quality 3D-aware cartoon faces with arbitrary styles.
