Deep Geometrized Cartoon Line Inbetweening
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.
6516523cd467ffdc0529102c
Li Siyao
6516523cd467ffdc0529102d
Tianpei Gu
extracted_confirmed
2023-09-30T21:08:41.225Z
6435ec65f3b08e267d9c9bbd
/avatars/7e846e0c91241898171aa35efd7264c0.svg
Tianpei Gu
user
gutianpei
6516523cd467ffdc0529102e
Weiye Xiao
6516523cd467ffdc0529102f
Henghui Ding
6516523cd467ffdc05291030
Ziwei Liu
6516523cd467ffdc05291031
Chen Change Loy
Deep Geometrized Cartoon Line Inbetweening
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.
AnimeInbet addresses line inbetweening by geometrically representing line drawings as graphs and using a vertex correspondence Transformer and visibility predictor for high-quality frame interpolation.
frame interpolation graph fusion vertex geometric embedding vertex correspondence Transformer vertex repositioning visibility predictor vectorization ground truth matching labels