Deformable One-shot Face Stylization via DINO Semantic Guidance
<|reference_start|>Deformable One-shot Face Stylization via DINO Semantic Guidance: This paper addresses the complex issue of one-shot face stylization, focusing on the simultaneous consideration of appearance and structure, where previous methods have fallen short. We explore deformation-aware face stylization that diverges from traditional single-image style reference, opting for a real-style image pair instead. The cornerstone of our method is the utilization of a self-supervised vision transformer, specifically DINO-ViT, to establish a robust and consistent facial structure representation across both real and style domains. Our stylization process begins by adapting the StyleGAN generator to be deformation-aware through the integration of spatial transformers (STN). We then introduce two innovative constraints for generator fine-tuning under the guidance of DINO semantics: i) a directional deformation loss that regulates directional vectors in DINO space, and ii) a relative structural consistency constraint based on DINO token self-similarities, ensuring diverse generation. Additionally, style-mixing is employed to align the color generation with the reference, minimizing inconsistent correspondences. This framework delivers enhanced deformability for general one-shot face stylization, achieving notable efficiency with a fine-tuning duration of approximately 10 minutes. Extensive qualitative and quantitative comparisons demonstrate our superiority over state-of-the-art one-shot face stylization methods. Code is available at https://github.com/zichongc/DoesFS<|reference_end|>
@article{zhou2024deformable,
    title={Deformable One-shot Face Stylization via DINO Semantic Guidance},
    author={Yang Zhou and Zichong Chen and Hui Huang},
    journal={arXiv preprint arXiv:2403.00459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00459},
    primaryClass={cs.CV}
}