Separating Shading and Reflectance From Cartoon Illustrations

Shading plays an important role in cartoon drawings to present the 3D lighting and depth information in a 2D image to improve the visual information and pleasantness. But it also introduces apparent challenges in analyzing and processing the cartoon drawings for different computer graphics and visio...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 7 vom: 10. Juni, Seite 3664-3679
1. Verfasser: Ma, Ziheng (VerfasserIn)
Weitere Verfasser: Li, Chengze, Liu, Xueting, Wu, Huisi, Wen, Zhenkun
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:Shading plays an important role in cartoon drawings to present the 3D lighting and depth information in a 2D image to improve the visual information and pleasantness. But it also introduces apparent challenges in analyzing and processing the cartoon drawings for different computer graphics and vision applications, such as segmentation, depth estimation, and relighting. Extensive research has been made in removing or separating the shading information to facilitate these applications. Unfortunately, the existing researches only focused on natural images, which are natively different from cartoons since the shading in natural images is physically correct and can be modeled based on physical priors. However, shading in cartoons is manually created by artists, which may be imprecise, abstract, and stylized. This makes it extremely difficult to model the shading in cartoon drawings. Without modeling the shading prior, in the paper, we propose a learning-based solution to separate the shading from the original colors using a two-branch system consisting of two subnetworks. To the best of our knowledge, our method is the first attempt in separating shading information from cartoon drawings. Our method significantly outperforms the methods tailored for natural images. Extensive evaluations have been performed with convincing results in all cases
Beschreibung:Date Revised 28.06.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2023.3239364