ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
<|reference_start|>ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis: 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.<|reference_end|>
@article{tang2024artnerf:,
    title={ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis},
    author={Zichen Tang, Hongyu Yang},
    journal={arXiv preprint arXiv:2404.13711},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13711},
    primaryClass={cs.CV}
}