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User-Guided Line Art Flat Filling With Split Filling Mechanism
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User-Guided Line Art Flat Filling With Split Filling Mechanism
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User-Guided Line Art Flat Filling with Split Filling Mechanism
Lvmin Zhang
Soochow University / Style2Paints Research
China
lvminzhang@acm.org
Chengze Li
The Chinese University of Hong Kong / Style2Paints Research
China
ljsabc@gmail.com
Edgar Simo-Serra
Waseda University / JST PRESTO
Japan
ess@waseda.jp
Yi Ji
Soochow University
China
jiyi@suda.edu.cn
Tien-Tsin Wong
The Chinese University of Hong Kong
China
ttwong@cse.cuhk.edu.hk
Chunping Liu
Soochow University
China
cpliu@suda.edu.cn
Abstract
Flat filling is a critical step in digital artistic content
creation with the objective of filling line arts with flat col-
ors. 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 simultane-
ously, leverages data-driven color generation to facilitate
content creation. This framework is based on a Split Fill-
ing 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 Convolu-
tional 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.
1. Introduction
Flat filling is a process to color line arts with fairly flat
colors according to the artists’ specifications. This technique
originates from the on-paper cartoon animation of the 1930s
and remains critical in the digital painting era, as these flat-
colored results exhibit great versatility in a wide variety of
art workflows. Not only these flat colors can be directly
blended with the line arts to create cartoon-like illustrations,
they can also be used as independent foundations without
blending the line arts for further adjustments towards more
sophisticated and plentiful digital paintings.
In computer vision and graphics, two broad paradigms of
user-guided line art colorization exist: traditional user scrib-
ble propagation and learning-based interactive colorization.
In the first paradigm, typical methods like LazyBrush [
28
]
and Manga Colorization [
23
] can explicitly and accurately
control the “influence areas” of user scribbles, i.e., the areas
where those scribbles should propagate and influence, by
matching the high-frequency/amplitude image constituents
with hand-defined prior/energy. In professional use cases,
the precise control of scribbles’ influence areas is indispens-
able for artists in editing detailed colors. Ideally, supposing
such influence areas are satisfactorily solved, the coloring
procedure will be free from color leakages, as they only prop-
9889
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