We aim to address a significant but understudied problem in the anime industry, namely the inbetweening of cartoon line drawings. Inbetweening involves generating intermediate frames between two black-and-white line drawings and is a time-consuming and expensive process that can benefit from automation. However, existing frame interpolation methods that rely on matching and warping whole raster images are unsuitable for line inbetweening and often produce blurring artifacts that damage the intricate line structures. To preserve the precision and detail of the line drawings, we propose a new approach, AnimeInbet, which geometrizes raster line drawings into graphs of endpoints and reframes the inbetweening task as a graph fusion problem with vertex repositioning. Our method can effectively capture the sparsity and unique structure of line drawings while preserving the details during inbetweening. This is made possible via our novel modules, i.e., vertex geometric embedding, a vertex correspondence Transformer, an effective mechanism for vertex repositioning and a visibility predictor. To train our method, we introduce MixamoLine240, a new dataset of line drawings with ground truth vectorization and matching labels. Our experiments demonstrate that AnimeInbet synthesizes high-quality, clean, and complete intermediate line drawings, outperforming existing methods quantitatively and qualitatively, especially in cases with large motions. Data and code are available at https://github.com/lisiyao21/AnimeInbet.

翻译：我们旨在解决动漫产业中一个显著但研究不足的问题，即卡通线条画的中间帧生成。中间帧生成涉及在两幅黑白线条画之间生成中间帧，这是一个耗时且昂贵的过程，可以通过自动化受益。然而，现有的依赖匹配和扭曲整个光栅图像的帧插值方法不适用于线条中间帧生成，且经常产生模糊伪影，破坏复杂的线条结构。为了保持线条画的精度和细节，我们提出了一种新方法AnimeInbet，它将光栅线条画几何化为端点和图的组合，并将中间帧生成任务重新定义为带有顶点重定位的图融合问题。我们的方法能够有效捕捉线条画的稀疏性和独特结构，同时在中间帧生成过程中保留细节。这得益于我们新颖的模块，即顶点几何嵌入、顶点对应Transformer、有效的顶点重定位机制以及可见性预测器。为了训练我们的方法，我们引入了MixamoLine240，这是一个带有真实向量化和匹配标签的线条画新数据集。我们的实验表明，AnimeInbet能够合成高质量、清晰且完整的中间线条画，在定量和定性上均优于现有方法，尤其是在大运动情况下。数据和代码可在https://github.com/lisiyao21/AnimeInbet获取。