Automatic colorization of anime line drawing has attracted much attention in recent years since it can substantially benefit the animation industry. User-hint based methods are the mainstream approach for line drawing colorization, while reference-based methods offer a more intuitive approach. Nevertheless, although reference-based methods can improve feature aggregation of the reference image and the line drawing, the colorization results are not compelling in terms of color consistency or semantic correspondence. In this paper, we introduce an attention-based model for anime line drawing colorization, in which a channel-wise and spatial-wise Convolutional Attention module is used to improve the ability of the encoder for feature extraction and key area perception, and a Stop-Gradient Attention module with cross-attention and self-attention is used to tackle the cross-domain long-range dependency problem. Extensive experiments show that our method outperforms other SOTA methods, with more accurate line structure and semantic color information.

翻译：近年来,动画线绘图的自动色彩化引起了人们的极大关注,因为它可以大大有利于动画行业。基于用户的“暗中”方法是线条绘制颜色化的主流方法,而基于参考的方法则提供了更直观的方法。尽管基于参考的方法可以改善参考图像和线条绘图的特征汇总,但颜色化结果在颜色一致性或语义对应方面并不具有说服力。在本文中,我们引入了一种关注的“暗线线绘制颜色化”模型,在这个模型中,使用一个频道和空间的“动态注意”模块来提高编码器的特征提取和关键区域感知能力,并使用一个带有交叉注意和自我注意的“停止偏重”模块来解决跨多边长距离依赖问题。广泛的实验表明,我们的方法比其他SOTA方法更符合常规方法,而有更精确的线结构和语义颜色信息。