Facial sketches are both a concise way of showing the identity of a person
and a means to express artistic intention. While a few techniques have recently
emerged that allow sketches to be extracted in different styles, they typically
rely on a large amount of data that is difficult to obtain. Here, we propose
StyleSketch, a method for extracting high-resolution stylized sketches from a
face image. Using the rich semantics of the deep features from a pretrained
StyleGAN, we are able to train a sketch generator with 16 pairs of face and the
corresponding sketch images. The sketch generator utilizes part-based losses
with two-stage learning for fast convergence during training for high-quality
sketch extraction. Through a set of comparisons, we show that StyleSketch
outperforms existing state-of-the-art sketch extraction methods and few-shot
image adaptation methods for the task of extracting high-resolution abstract
face sketches. We further demonstrate the versatility of StyleSketch by
extending its use to other domains and explore the possibility of semantic
editing. The project page can be found in
this https URL

通过预训练的 StyleGAN 模型以及基于部分损失的两阶段学习技术，提出了 StyleSketch 方法，通过 16 对面部图像和相应的素描图像，训练了一个能够提取高分辨率风格化素描的生成器，并通过比较表明 StyleSketch 在提取高分辨率抽象人脸素描方面优于现有的最先进的素描提取方法和少量样本图像适应方法，同时还探索了其在其他领域的可用性和语义编辑的可能性。