User-guided line art flat filling with split filling mechanism
Author(s)
Author(s)
Zhang, L.
Simo-Serra, E.
Ji, Y.
Wong, T.-T.
Liu, C.
Date Issued
2021
Publisher
Computer Vision Foundation
Related Publication(s)
Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Start page
9889
End page
9898
Abstract
Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.
SFU Affiliated Publication
No
Availability at SFU Library
No database links found.