  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
