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MangaNinja: Line Art Colorization with Precise Reference Following

About

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.

Zhiheng Liu, Ka Leong Cheng, Xi Chen, Jie Xiao, Hao Ouyang, Kai Zhu, Yu Liu, Yujun Shen, Qifeng Chen, Ping Luo• 2025

Related benchmarks

TaskDatasetResultRank
Lineart ColorizationLineart Colorization 900 samples (test)
Image Alignment Score90.25
23
Image-referenced Sketch ColorizationTriplet 50K (val)
FID42.85
7
Local ColourisationPlace365 Indoor
FID134.6
5
Local ColourisationPlace365 Outdoor
FID127.7
5
Local ColourisationPascalVOC 2012
FID289.2
5
Local ColourisationDanbooru 2023
FID304.2
5
Image ColorizationAnimation Film Image Pairs 1.5K (test)
SSIM54.3
4
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