MangaNinja: Line Art Colorization with Precise Reference Following
Zhiheng Liu, Ka Leong Cheng, Xi Chen, Jie Xiao, Hao Ouyang, Kai Zhu, Yu Liu, Yujun Shen, Qifeng Chen, Ping Luo
2025-01-15
Summary
This paper talks about MangaNinja, a new AI tool that can automatically color black and white line drawings of anime and manga characters. It uses a special technique to match colors from a reference image to the line art, making the coloring process faster and more accurate.
What's the problem?
Coloring line art for anime and manga can be really time-consuming and tricky, especially when trying to match the colors to a specific reference image. Current AI tools aren't great at getting all the little details right or handling complicated cases where the line art and reference image are quite different.
What's the solution?
The researchers created MangaNinja, which uses two clever tricks to solve this problem. First, it has a 'patch shuffling module' that helps it understand how different parts of the reference image match up with the line art. Second, it has a 'point-driven control scheme' that lets users guide the coloring process by clicking on specific points. They tested MangaNinja against other AI coloring tools and found it did a much better job at precise coloring. It can even handle tricky situations like coloring one character to look like another, or blending colors from multiple reference images.
Why it matters?
This matters because it could save anime and manga artists a ton of time and effort. Instead of spending hours carefully coloring each frame or page, they could use MangaNinja to do most of the work automatically. This could speed up the production of anime and manga, potentially leading to more content for fans. It's also a cool example of how AI can be used as a creative tool to help artists, rather than replace them. The technology behind MangaNinja might also be useful for other kinds of digital art and design work beyond just anime and manga.
Abstract
Derived from diffusion models, MangaNinjia specializes in the task of reference-guided line art colorization. We incorporate two thoughtful designs to ensure precise character detail transcription, including a patch shuffling module to facilitate correspondence learning between the reference color image and the target line art, and a point-driven control scheme to enable fine-grained color matching. Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. We further showcase the potential of the proposed interactive point control in handling challenging cases, cross-character colorization, multi-reference harmonization, beyond the reach of existing algorithms.