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，动漫线稿的自动着色技术因其对动画产业的显著推动作用而备受关注。基于用户提示的方法是目前线稿着色的主流方案，而基于参考图像的方法则提供了更直观的途径。然而，尽管参考图像方法能增强参考图像与线稿的特征融合，其在色彩一致性和语义对应性方面的着色效果仍不理想。本文提出了一种基于注意力的动漫线稿着色模型，其中利用通道-空间卷积注意力模块提升编码器在特征提取和关键区域感知方面的能力，并通过融合交叉注意力与自注意力的停止梯度注意力模块解决跨域长程依赖问题。大量实验表明，我们的方法在保持更精确的线条结构和语义色彩信息方面优于现有最优方法。