  Line art colorization is expensive and challenging to automate. A GAN
approach is proposed, called Tag2Pix, of line art colorization which takes as
input a grayscale line art and color tag information and produces a quality
colored image. First, we present the Tag2Pix line art colorization dataset. A
generator network is proposed which consists of convolutional layers to
transform the input line art, a pre-trained semantic extraction network, and an
encoder for input color information. The discriminator is based on an auxiliary
classifier GAN to classify the tag information as well as genuineness. In
addition, we propose a novel network structure called SECat, which makes the
generator properly colorize even small features such as eyes, and also suggest
a novel two-step training method where the generator and discriminator first
learn the notion of object and shape and then, based on the learned notion,
learn colorization, such as where and how to place which color. We present both
quantitative and qualitative evaluations which prove the effectiveness of the
proposed method.
