## 1 Introduction  
Line art colorization is an expensive and labor-intensive process especially in the animation and comics industry due to the repetitive tasks of the job. In the webtoon (web-publishing comics) industry, such an iterative work of colorizing many line art drawings makes it prone to coloring mistakes, as well as exhausting much cost and time. To mitigate this problem, automating the procedure of coloring should be considered to reduce the amount of actual coloring labor. Recently, many challenges and improvements for automating line art colorization have appeared, and most of the studies have been based on Generative Adversarial Networks (GANs) [1]. The GAN in the past only generated random images based on the training dataset, which cannot be processed based on the user's desired sketch input. However, Pix2 pix [2], a modified method of GANs, which can generate images based on an input vector, has motivated research into automatic line art colorization. For example, the methods of [3, 4, 5] give user-guided suggestions about the desired data for colors and locations. The two methods of [6, 7] provided the desired style for colorization using the data of reference images. Even though these methods generate a fully colorized image for the input line art image, their quality and details are not sufficient for application in the webtoon industry. For example, as illustrated in Figure 1, a currently available technique,here Style2 points [7], produces unmatching color tones and blurring in the generated images compared to the original designs. Of course, we could have obtained a better colorization, but hyper-parameter tuning and training of the model were barely possible in a reasonable testing time. Practically, it is a minimum requirement that the generated figures have almost an exact color match to their original character designs, if they are to be used in commercial illustration. This is not limited to the webtoon industry. Figure 2 shows examples of coloring mistakes in commercial animation; notice that the dress color changes instantly in the animation, which should not have occurred. Our goal in this paper is to remove such colorization mistakes in the mass production of webtoons or animations and to reduce the amount of time spent on the whole process of line art coloring. To accomplish this goal, we had to find a method to colorize a particular character with high-quality color matches, rather than colorizing random characters with hardly matching colors. Observing the colorization results from previous methods, we see mainly two features to be upgraded for the colorization used in the webtoon production pipeline. First of all, the coloring should be confined accurately by the boundaries given from the line art input. This will allow the artists to seamlessly do the job of adding highlights and details to the output when the output of automatic colorization needs a few more details. Second, the generative colorization model should be able to learn the particular character's pattern from only a small amount of input data. The number of original images created by the  
Figure 1: Original images used in a webtoon and a set of generated images by Style2 points, a line art colorization network [7]. Figure 2: The same character in a sequence is mis-colored from bluish clothes to yellow clothes. This kind of mistake can be easily found in TV series animations due to the lack of time and labor [8]. chief artist is not many, usually about 10 or less than that. Furthermore, using only a few data can make it profitable for making multiple colorization models for each character. To achieve our goal, we propose a new GAN-based neural network model, Seg2 pix The model generates a segmented image to be used in generating a high-quality colorized image. The segmented image provides data that clearly distinguish the parts of the character according to borderlines of the line art. As shown in Figure 3 the segmented image shows an example of the first generated result of our Seg2 pix model.