Stylized Face Sketch Extraction via Generative Prior with Limited Data
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
Description
CCS Concepts: Computing methodologies -> Artificial intelligence; Computer vision; Computer vision
@article{10.1111:cgf.15045,
journal = {Computer Graphics Forum},
title = {{Stylized Face Sketch Extraction via Generative Prior with Limited Data}},
author = {Yun, Kwan and Seo, Kwanggyoon and Seo, Chang Wook and Yoon, Soyeon and Kim, Seongcheol and Ji, Soohyun and Ashtari, Amirsaman and Noh, Junyong},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15045}
}