用任意数量参考图实现动漫线稿精准上色，提升复杂场景适应性。
DACoN: DINO for Anime Paint Bucket Colorization with Any Number of Reference Images
- 融合大模型语义与CNN空间特征，捕捉局部语义信息。
- 支持多参考图像输入，显著提升遮挡和视角变化下的上色准确率。
- 适合动漫自动化生产，尤其适用于复杂分镜场景的上色任务。
自动为线稿上色已广泛用于降低手绘动漫制作的人工成本。尽管深度学习方法（包括图像/视频生成和基于特征的对应）提升了准确性，但在遮挡、姿态变化和视角差异下仍表现不佳。为此，我们提出DACoN框架，利用基础模型捕捉线稿中的部件级语义，即使在缺乏纹理的线稿中也能有效工作。该方法将基础模型的低分辨率语义特征与CNN的高分辨率空间特征融合，实现细粒度且鲁棒的特征提取。不同于以往依赖Multiplex Transformer、仅支持一到两个参考图像的方法，DACoN突破此限制，可接受任意数量的参考图像。定量与定性评估表明，使用多个参考图像能显著提升上色效果。代码与模型已在https://github.com/kzmngt/DACoN发布。
原文摘要 · Abstract (English)
Automatic colorization of line drawings has been widely studied to reduce the labor cost of hand-drawn anime production. Deep learning approaches, including image/video generation and feature-based correspondence, have improved accuracy but struggle with occlusions, pose variations, and viewpoint changes. To address these challenges, we propose DACoN, a framework that leverages foundation models to capture part-level semantics, even in line drawings. Our method fuses low-resolution semantic features from foundation models with high-resolution spatial features from CNNs for fine-grained yet robust feature extraction. In contrast to previous methods that rely on the Multiplex Transformer and support only one or two reference images, DACoN removes this constraint, allowing any number of references. Quantitative and qualitative evaluations demonstrate the benefits of using multiple reference images, achieving superior colorization performance. Our code and model are available at https://github.com/kzmngt/DACoN.
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