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MangaNinja: Line Art Colorization with Precise Reference Following

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arxiv 2501.08332 v1 pith:S4DYHTWP submitted 2025-01-14 cs.CV

classification cs.CV
keywords colorizationlineprecisecolorcontrolreferencealgorithmsbenchmark
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MagicAnime is a 400k-clip multimodal cartoon dataset with hierarchical annotations and benchmarks for image-to-video, pose-driven, face reenactment, and audio-driven animation generation.

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