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Visualizing Visual Adaptation
Published on: April 24, 2017
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AnimeDiffusion: Anime Diffusion Colorization.
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.

