Stylized-Colorization for Line-Arts

Tzu-Ting Fang, MinhDuc Vo, Akihiro Sugimoto, Shang-Hong Lai

Abstract:

We address a novel problem of stylized-colorization
which colorizes a given line art using a given coloring style in text.
This problem can be stated as multi-domain image translation
and is more challenging than the current colorization problem
because it requires not only capturing the illustration distribution
but also satisfying the required coloring styles specific to anime
such as lightness, shading, or saturation. We propose a GAN-based end-to-end model for stylized-colorization where the model
has one generator and two discriminators. Our generator is based
on the U-Net architecture and receives a pair of a line art and a
coloring style in text as its input to produce a stylized-colorization
image of the line art. Two discriminators, on the other hand,
share weights at early layers to judge the stylized-colorization
image in two different aspects: one for color and one for style.
One generator and two discriminators are jointly trained in
an adversarial and end-to-end manner. Extensive experiments
demonstrate the effectiveness of our proposed model.