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 https://kwanyun.github.io/stylesketch_project.

翻译：人脸素描既是展示人物身份的一种简洁方式，也是表达艺术意图的手段。近期虽出现少数能以不同风格提取素描的技术，但它们通常依赖难以获取的大量数据。本文提出StyleSketch——一种从人脸图像中提取高分辨率风格化素描的方法。通过利用预训练StyleGAN深层特征的丰富语义，我们仅用16对人脸及对应素描图像即可训练素描生成器。该生成器采用基于部位损失的两阶段学习策略，在训练过程中实现快速收敛，以获取高质量素描提取效果。通过系列对比实验，我们证明StyleSketch在提取高分辨率抽象人脸素描任务上优于现有最先进的素描提取方法与少样本图像适配方法。进一步通过扩展至其他领域并探索语义编辑可能性，我们展示了StyleSketch的多功能性。项目页面见https://kwanyun.github.io/stylesketch_project。