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Line art colorization is expensive and challenging to automate. A GANapproach is proposed, called Tag2Pix, of line art colorization which takes asinput a grayscale line art and color tag information and produces a qualitycolored image. First, we present the Tag2Pix line art colorization dataset. Agenerator network is proposed which consists of convolutional layers totransform the input line art, a pre-trained semantic extraction network, and anencoder for input color information. The discriminator is based on an auxiliaryclassifier GAN to classify the tag information as well as genuineness. Inaddition, we propose a novel network structure called SECat, which makes thegenerator properly colorize even small features such as eyes, and also suggesta novel two-step training method where the generator and discriminator firstlearn 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 bothquantitative and qualitative evaluations which prove the effectiveness of theproposed method.

Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss