Deep style transfer for line drawings
Author(s)
Author(s)
Wu, W.
Wu, H.
Wen, Z.
Date Issued
2021
Publisher
AAAI Press
Related Publication(s)
Proceedings of the 35th AAAI Conference on Artificial Intelligence
Volume
35
Issue
1
Start page
353
End page
361
Abstract
Line drawings are frequently used to illustrate ideas and concepts in digital documents and presentations. To compose a line drawing, it is common for users to retrieve multiple line drawings from the Internet and combine them as one image. However, different line drawings may have different line styles and are visually inconsistent when put together. In order that the line drawings can have consistent looks, in this paper, we make the first attempt to perform style transfer for line drawings. The key of our design lies in the fact that centerline plays a very important role in preserving line topology and extracting style features. With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. Results and statistics show that our method significantly outperforms the existing methods both visually and quantitatively.
SFU Affiliated Publication
No
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