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MagicColor - A multi-instance line art coloring framework developed by the Hong Kong University of Science and Technology.

MagicColor is a multi-instance line art coloring framework developed by the Hong Kong University of Science and Technology, supporting efficient color addition to line art. MagicColor addresses the problem of insufficient multi-instance data by employing a self-supervised training strategy and an instance-guided module...

What is MagicColor?

MagicColor is a multi-instance line art coloring framework developed by the Hong Kong University of Science and Technology, supporting efficient color addition to line art. Based on a self-supervised training strategy and an instance-guided module, MagicColor addresses the problem of insufficient multi-instance data, achieving precise instance-level color control. Compared to traditional methods, MagicColor can complete the coloring of multi-instance line art in a single forward propagation, significantly improving coloring efficiency and making it suitable for animation production and digital art creation.

MagicColor's main functions

  • Multi-instance line drawing coloringMagicColor can handle sketch coloring tasks for multiple instances simultaneously, assigning an accurate color to each instance.
  • Automated coloringAutomatically converts line art into richly colored images.
  • Reference Image DrivenThe user provides a reference image, and MagicColor colors the sketch according to the colors and style of the reference image, ensuring that the output image is visually consistent with the reference image.
  • Instance-level controlPrecisely control the color of each instance, maintaining accuracy of detail and color in complex multi-instance scenarios.
  • Edge enhancementThe model focuses on the edges and high-frequency regions of the image during coloring, improving the accuracy and visual quality of coloring.

MagicColor's technical principles

  • Diffusion Model BasicsMagicColor is based on a pre-trained diffusion model, which leverages the powerful generative and semantic consistency-preserving capabilities of diffusion models to convert sketches into color images.
  • Self-supervised training strategyTo address the issue of insufficient multi-instance training data, MagicColor employs a two-stage self-supervised training strategy. The first stage uses a single reference image for training, while the second stage generates multi-instance data based on operations such as random fusion and scaling, further optimizing the model's multi-instance coloring capabilities.
  • Instance Bootstrap ModuleThe module extracts instance features from the reference image and aligns them with the sketch to achieve precise instance-level color control. Using features extracted by DINOv2 and ROI alignment techniques, it ensures that the color of each instance is accurately transferred to the target sketch.
  • Edge loss and color matchingTo improve the visual quality of colorization, MagicColor introduces an edge loss function, allowing the model to focus on the edges and structural details of the image. Based on color matching technology, the model establishes a pixel-level correspondence between the reference image and the target sketch, ensuring accurate color transfer.
  • Dual UNet architectureMagicColor combines two UNet architectures: one for processing the reference image and the other for generating the final color image. This allows the model to better integrate the color information from the reference image and the structural information from the sketch, resulting in high-quality colorization results.

MagicColor's project address

Application Scenarios of MagicColor

  • Animation ProductionQuickly colorize multi-character sketches, improving production efficiency and maintaining a consistent color style.
  • Digital art creationQuickly colorize sketches, inspire creativity, and generate multiple color versions.
  • Game developmentQuickly generate color versions of characters and scenes, improving development efficiency and generating stylized assets.
  • Education and TrainingAs a teaching tool, it helps students quickly understand color theory and improve their ability to use color.
  • Advertising and MediaQuickly generate high-quality color sketches to meet advertising design needs and enhance visual appeal and brand consistency.
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