Deep Geometrized Cartoon Line Inbetweening
Li Siyao Tianpei Gu Weiye Xiao Henghui Ding Ziwei Liu Chen Change Loy
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.