Stylized Face Sketch Extraction via Generative Prior with Limited Data

Simple item page
dc.contributor.authorYun, Kwanen_US
dc.contributor.authorSeo, Kwanggyoonen_US
dc.contributor.authorSeo, Chang Wooken_US
dc.contributor.authorYoon, Soyeonen_US
dc.contributor.authorKim, Seongcheolen_US
dc.contributor.authorJi, Soohyunen_US
dc.contributor.authorAshtari, Amirsamanen_US
dc.contributor.authorNoh, Junyongen_US
dc.contributor.editorBermano, Amit H.en_US
dc.contributor.editorKalogerakis, Evangelosen_US
dc.date.accessioned2024-04-30T09:09:59Z
dc.date.available2024-04-30T09:09:59Z
dc.date.issued2024
dc.description.abstractFacial 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.en_US
dc.description.number2
dc.description.sectionheadersNeural Texture and Image Synthesis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15045
dc.identifier.issn1467-8659
dc.identifier.pages14 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15045
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15045
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Artificial intelligence; Computer vision; Computer vision
dc.subjectComputing methodologies
dc.subjectArtificial intelligence
dc.subjectComputer vision
dc.subjectComputer vision
dc.titleStylized Face Sketch Extraction via Generative Prior with Limited Dataen_US
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
v43i2_50_15045.pdf
Size:
15.07 MB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
paper1069.pdf
Size:
35.42 MB
Format:
Adobe Portable Document Format
Download
Collections
43-Issue 2