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

TL;DR
This paper introduces AnimeInbet, a novel graph-based approach for automatic inbetweening of line drawings in animation, effectively capturing line details and outperforming existing methods especially with large motions.
Contribution
The paper proposes a new geometrization-based method, AnimeInbet, with novel modules and a new dataset, addressing the limitations of raster image interpolation for line drawings.
Findings
AnimeInbet produces high-quality intermediate frames.
Outperforms existing methods quantitatively and qualitatively.
Effective in cases with large motions.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Vision and Imaging · Generative Adversarial Networks and Image Synthesis · Human Motion and Animation
MethodsAttention Is All You Need · Linear Layer · Position-Wise Feed-Forward Layer · Residual Connection · Label Smoothing · Absolute Position Encodings · Dense Connections · Layer Normalization · Multi-Head Attention · Byte Pair Encoding
