Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2020
Abstract
Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. In the second stage we draw lines on the structural edges of the flattened cartoon with the fully supervised line drawing objective and unsupervised edge augmenting loss. We collect a cartoon-line dataset with line tracing, and it serves as the starting point for preparing abstraction and line drawing data. We have evaluated the proposed method on a large number of photographs, by converting them to three different cartoon styles. Our method substantially outperforms state-of-the-art methods in terms of visual quality quantitatively and qualitatively.
Keywords
Computing, methodologies, Neural networks, Image processing
Discipline
Information Security
Research Areas
Information Systems and Management
Publication
Computer Graphics Forum
Volume
39
Issue
7
First Page
587
Last Page
599
ISSN
0167-7055
Identifier
10.1111/cgf.14170
Publisher
Wiley
Citation
LI, Simin; WEN, Qiang; ZHAO, Shuang; SUN, Zixun; and HE, Shengfeng.
Two-stage photograph cartoonization via line tracing. (2020). Computer Graphics Forum. 39, (7), 587-599.
Available at: https://ink.library.smu.edu.sg/sis_research/7842
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/cgf.14170