title: MangaNinja: Line Art Colorization with Precise Reference Following
summary: 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.
paper_authors._id: 678727aadd2e5dbecdf08fe3
paper_authors.name: Zhiheng Liu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/bb52ecd22ca4b49157f8668be35409e7.svg
paper_authors.fullname: Zhiheng Liu
paper_authors.type: user
paper_authors.user: Johanan0528
paper_authors._id: 678727aadd2e5dbecdf08fe4
paper_authors.name: Ka Leong Cheng
paper_authors.status: admin_assigned
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paper_authors.fullname: Ka Leong Cheng
paper_authors.type: user
paper_authors.user: felixcheng97
paper_authors._id: 678727aadd2e5dbecdf08fe5
paper_authors.name: Xi Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: xichen
paper_authors.type: user
paper_authors.user: xichenhku
paper_authors._id: 678727aadd2e5dbecdf08fe6
paper_authors.name: Jie Xiao
paper_authors.status: admin_assigned
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paper_authors._id: 646b2767ec9a61e8717a41a7
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paper_authors.fullname: jiexiao
paper_authors.type: user
paper_authors.user: jiexiao
paper_authors._id: 678727aadd2e5dbecdf08fe7
paper_authors.name: Hao Ouyang
paper_authors._id: 678727aadd2e5dbecdf08fe8
paper_authors.name: Kai Zhu
paper_authors._id: 678727aadd2e5dbecdf08fe9
paper_authors.name: Yu Liu
paper_authors._id: 678727aadd2e5dbecdf08fea
paper_authors.name: Yujun Shen
paper_authors._id: 678727aadd2e5dbecdf08feb
paper_authors.name: Qifeng Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:43:50.764Z
paper_authors._id: 6467b121e7a6a374fd19b44b
paper_authors.avatarUrl: /avatars/3f2874d58986d651aef55e3408b05700.svg
paper_authors.fullname: Qifeng Chen
paper_authors.type: user
paper_authors.user: cqf
paper_authors._id: 678727aadd2e5dbecdf08fec
paper_authors.name: Ping Luo
paper_title: MangaNinja: Line Art Colorization with Precise Reference Following
paper_summary: 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.
paper_ai_keywords: diffusion models reference-guided line art colorization patch shuffling module correspondence learning point-driven control scheme fine-grained color matching self-collected benchmark cross-character colorization multi-reference harmonization