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Learning Inclusion Matching For Animation Paint Bucket Colorization
Learning Inclusion Matching For Animation Paint Bucket Colorization
Overview :
This algorithm aims to simplify the animation coloring process. Traditionally, digital painters need to manually color each frame for a line art animation, which is very time-consuming and labor-intensive. This algorithm only requires the painter to color the first frame, and it can automatically propagate the color to all subsequent frames, significantly improving work efficiency. The core of the algorithm is a novel inclusion relationship matching module that can accurately capture details such as object deformation and occlusion in animation, ensuring the accuracy of the coloring. This algorithm has developed a specialized dataset for training, which can fully exploit the algorithm's coloring capabilities. Compared to existing technologies, this algorithm demonstrates superior coloring quality and robustness.
Target Users :
This algorithm speeds up the coloring process in animation production workflows, specifically the line drawing coloring stage.
Use Cases
When producing hand-drawn animation series, using this algorithm can greatly shorten the coloring time.
When producing animation shorts, using the algorithm to assist with coloring can significantly reduce labor costs.
Digital animators can use this algorithm to alleviate the heavy burden of coloring.
Features
Color subsequent frames automatically after coloring the first frame.
Inclusion relationship matching module accurately captures object deformation.
Dedicated animation coloring training dataset.
Superior coloring quality compared to existing technologies.
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