The process of drawing digital comics and animations is a complex process that involves multiple stages. Flat-coloring, the task of filling segmented regions in a line art image with uniform tone and hue, is a particularly time-consuming and labor-intensive task. We have identified that artists suffer from not only adjusting colors in overflowing regions due to line discontinuity but also finding to replace misaligned pixels near the line due to region-bleeding problems (aliasing issues). To address these issues, we propose a holistic data generation pipeline (FlatGAN-DG) that awares the region of line discontinuity and augments the input sketch image to build robust models for noise. In addition, we propose a real-time post-processing method (FlatGAN-PP) that automatically finds and replaces miscolored pixels to alleviate the region-bleeding problems (aliasing issues). To enhance inference speed, we build FlatGAN, which shares the parameters of a generator to predict the foreground, background, and trimap at once to learn in a multi-task manner. Our experimental results show that our method outperforms other rule-and learning-based methods on three different datasets with different painting styles. To evaluate the segmented regions, we collect datasets with the annotation of split-score, merge-hard-score, and merge-easy-score. We also introduce a new evaluation metric (Region Score) on these datasets, validating the efficacy of our methods through a user study. Code is available at https://github.com/hanish3464/FlatGAN.

Automatic line-art colorization is a demanding research field owing to its expensive and labor-intensive workload. Learning-based approaches have lately emerged to improve the quality of colorization. To handle the lack of paired data in line art and color images, sketch extraction has been widely adopted. This study primarily focuses on the resizing process applied within the sketch extraction procedure, which is essential for normalizing input sketches of various sizes to the target size of the colorization model. We first analyze the inherent risk in a conventional resizing strategy, i.e., early-resizing, which places the resizing step before the line detection process to ensure the practicality. Although the strategy is extensively used, it involves an often overlooked risk of significantly degrading the generalization of the colorization model. Thus, we propose a late-resizing strategy in which resizing is applied after the line detection step. The proposed late-resizing strategy has three advantages: prevention of a quality degradation in the color image, augmentation for downsizing artifacts, and alleviation of look-ahead bias. In conclusion, we present both quantitative and qualitative evaluations on representative learning-based line-art colorization methods, which verify the effectiveness of the proposed method in the generalization of the colorization model.