Learning Inclusion Matching for Animation Paint Bucket Colorization
CVPR 2024(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要