Learning Inclusion Matching for Animation Paint Bucket Colorization

TL;DR

This paper presents a novel learning-based inclusion matching approach for animation paint bucket colorization, improving accuracy over existing segment matching methods by understanding segment inclusion relationships.

Contribution

The paper introduces a two-stage pipeline with a coarse color warping and inclusion matching modules, along with a new dataset for training and evaluating animation colorization models.

Findings

Outperforms existing segment matching techniques in accuracy.

Effectively handles occlusion and wrinkles in animation frames.

Provides a new dataset, PaintBucket-Character, for training and evaluation.

Abstract

Colorizing line art is a pivotal task in the production of hand-drawn cel animation. This typically involves digital painters using a paint bucket tool to manually color each segment enclosed by lines, based on RGB values predetermined by a color designer. This frame-by-frame process is both arduous and time-intensive. Current automated methods mainly focus on segment matching. This technique migrates colors from a reference to the target frame by aligning features within line-enclosed segments across frames. However, issues like occlusion and wrinkles in animations often disrupt these direct correspondences, leading to mismatches. In this work, we introduce a new learning-based inclusion matching pipeline, which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences. Our method features a two-stage pipeline that integrates a coarse color warping module with an inclusion matching module, enabling more nuanced and accurate colorization. To facilitate the training of our network, we also develope a unique dataset, referred to as PaintBucket-Character. This dataset includes rendered line arts alongside their colorized counterparts, featuring various 3D characters. Extensive experiments demonstrate the effectiveness and superiority of our method over existing techniques.