Paint Bucket Colorization Using Anime Character Color Design Sheets
Yuekun Dai, Qinyue Li, Shangchen Zhou, Yihang Luo, Chongyi Li, Chen, Change Loy

TL;DR
This paper presents a novel inclusion matching approach for automated line art colorization in animation, improving accuracy and consistency in keyframe and frame-to-frame colorization tasks by understanding segment relationships.
Contribution
The work introduces inclusion matching combined with segment parsing and color warping, along with a new dataset, to enhance automated colorization accuracy in animation production.
Findings
Outperforms existing methods in keyframe and frame colorization tasks.
Achieves more accurate and consistent coloring across animation frames.
Demonstrates effectiveness on custom benchmarks and hand-drawn animations.
Abstract
Line art colorization plays a crucial role in hand-drawn animation production, where digital artists manually colorize segments using a paint bucket tool, guided by RGB values from character color design sheets. This process, often called paint bucket colorization, involves two main tasks: keyframe colorization, where colors are applied according to the character's color design sheet, and consecutive frame colorization, where these colors are replicated across adjacent frames. Current automated colorization methods primarily focus on reference-based and segment-matching approaches. However, reference-based methods often fail to accurately assign specific colors to each region, while matching-based methods are limited to consecutive frame colorization and struggle with issues like significant deformation and occlusion. In this work, we introduce inclusion matching, which allows the…
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Taxonomy
TopicsHandwritten Text Recognition Techniques
MethodsColorization · Focus
