What is MangaNinja?

MangaNinja is a line art coloring method based on reference images, featuring precise matching and detailed control capabilities. Through innovative patch rearrangement modules and point-driven control schemes, it enhances coloring accuracy and image quality. It can handle diverse coloring challenges, including extreme poses and coordination of multiple reference images, achieving high-quality interactive coloring experiences.

Main Features of MangaNinja

• Reference-based Line Art Coloring: Provides coloring guidance for line art through reference images, achieving precise color matching.
• Accurate Character Detail Transcription: The patch rearrangement module promotes correspondence learning between reference color images and target line art, enhancing the model's automatic matching capability.
• Fine-grained Interactive Control: The point-driven control scheme allows users to perform precise color matching, especially excelling in complex scenes.
• Handling Complex Scenes: Effectively resolves issues such as significant changes in character poses or missing details. When multiple objects are involved, point guidance effectively prevents color confusion.
• Harmonious Coloring with Multiple Reference Images: Users can select specific areas of multiple reference images to guide the coloring of various elements in the line art, effectively resolving conflicts between similar visual elements.

Technical Principles of MangaNinja

Architecture Design

• Reference U-Net: Given the strict detail requirements for line art coloring, MangaNinja introduces a Reference U-Net, using VAE to encode reference images into 4-channel latent representations, then extracting multi-level features to fuse with the main Denoising U-Net.
• Denoising U-Net: The Denoising U-Net is one of MangaNinja's core components, responsible for fusing the encoded reference image features with the line art, gradually removing noise to generate the final colored image.

Innovative Design

• Patch Rearrangement Module: The patch rearrangement module is one of MangaNinja's key innovations. It promotes correspondence learning between reference color images and target line art by segmenting the reference image into multiple small patches and rearranging them, enhancing the model's automatic matching capability.
• Point-driven Control Scheme: Users can predefine specific points on the reference image and line art to guide the coloring process, achieving fine-grained color matching.

Training Strategy

• Conditional Dropout: During training, randomly drop part of the reference image features, forcing the model to learn more robust matching capabilities.
• Progressive Patch Shuffling: Gradually increase the complexity of patch shuffling, allowing the model to learn effective matching strategies at different stages.

MangaNinja Project Address

• Project Website: https://johanan528.github.io/MangaNinjia
• Github Repository: https://github.com/ali-vilab/MangaNinjia
• arXiv Technical Paper: https://arxiv.org/pdf/2501.08332

Application Scenarios of MangaNinja

• Comic Creation: Comic creators can use MangaNinja to quickly color newly drawn line art. By inputting line art and reference images, MangaNinja can automatically recognize and apply colors, achieving high-precision coloring effects.
• Illustration Design: MangaNinja's point-driven control scheme allows users to fine-tune colors, ensuring that every detail meets design requirements.
• Graphic Design: Designers can use MangaNinja's multi-reference coordination function to extract colors from multiple reference images, creating unique design works.
• Digital Art Creation: Digital artists can use MangaNinja to quickly complete the coloring of line art, dedicating more time and effort to creative conception and detail adjustments.