Learning Inclusion Matching for Animation Paint Bucket Colorization

• URL: http://arxiv.org/abs/2403.18342v1
• Date: Wed, 27 Mar 2024 08:32:48 GMT
• Title: Learning Inclusion Matching for Animation Paint Bucket Colorization
• Authors: Yuekun Dai, Shangchen Zhou, Qinyue Li, Chongyi Li, Chen Change Loy,
• Abstract summary: We introduce a new learning-based inclusion matching pipeline, which directs the network to comprehend the inclusion relationships between segments. Our method features a two-stage pipeline that integrates a coarse color warping module with an inclusion matching module. To facilitate the training of our network, we also develope a unique dataset, referred to as PaintBucket-Character.
• Score: 76.4507878427755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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