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Related Experiment Video

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Visualizing Visual Adaptation

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AnimeDiffusion: Anime Diffusion Colorization.

Yu Cao, Xiangqiao Meng, P Y Mok

IEEE Transactions on Visualization and Computer Graphics

|January 23, 2024

Summary

AnimeDiffusion revolutionizes anime face line drawing colorization using a novel hybrid diffusion model. This AI approach achieves high-quality, consistent results, outperforming existing methods.

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Area of Science:

Computer Vision
Artificial Intelligence
Digital Art

Background:

Manual anime line drawing colorization is labor-intensive.
Existing reference-based methods, often GAN-based, struggle with high-quality results.
Diffusion models offer potential for controlled image synthesis.

Purpose of the Study:

Introduce AnimeDiffusion, a novel diffusion model for reference-based anime face line drawing colorization.
Address limitations of current generative adversarial network (GAN) approaches.
Improve control and quality in automatic image colorization.

Main Methods:

Propose AnimeDiffusion, a hybrid diffusion model for anime face line drawing colorization.
Implement a two-phase end-to-end training strategy: classifier-free guidance and reference image color tone updating.
Utilize a new anime face line drawing colorization benchmark dataset.

Main Results:

AnimeDiffusion achieves superior colorization with semantic correspondence and color consistency.
The hybrid training accelerates convergence and enhances performance.
Demonstrated strong generalization across different line styles.
Outperformed state-of-the-art GAN-based and diffusion-based methods in experiments and user studies.

Conclusions:

AnimeDiffusion represents a significant advancement in automatic anime line drawing colorization.
The hybrid diffusion approach offers improved quality, control, and efficiency.
The introduced dataset facilitates further research in this domain.