Region-Wise Correspondence Prediction between Manga Line Art Images
This addresses a fundamental challenge in high-level manga processing for tasks like colorization and animation, though it is incremental as it builds on existing methods for correspondence prediction.
The paper tackles the problem of predicting region-wise correspondences between manga line art images, which lack rich visual cues, by proposing a Transformer-based framework trained on automatically generated data, achieving 78.4-84.4% region-level accuracy.
Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization and in-between frame generation. Unlike natural images that contain rich visual cues, manga line art consists only of sparse black-and-white strokes, making it challenging to determine which regions correspond across images. In this work, we introduce a new task: predicting region-wise correspondence between raw manga line art images without any annotations. To address this problem, we propose a Transformer-based framework trained on large-scale, automatically generated region correspondences. The model learns to suppress noisy matches and strengthen consistent structural relationships, resulting in robust patch-level feature alignment within and across images. During inference, our method segments each line art and establishes coherent region-level correspondences through edge-aware clustering and region matching. We construct manually annotated benchmarks for evaluation, and experiments across multiple datasets demonstrate both high patch-level accuracy and strong region-level correspondence performance, achieving 78.4-84.4% region-level accuracy. These results highlight the potential of our method for real-world manga and animation applications.