一个两阶段的无监督的GAN方法,用于动漫-漫画翻译
IEEE computer graphics and applications
|May 6, 2025
概括
本研究探讨了无监督的图像对图像翻译,用于使用参考漫画书将动漫艺术转换为漫画风格. 这种新的两阶段方法简化了模型,并产生了高质量的,无监督的漫画翻译.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 数字艺术 数字艺术 数字艺术
背景情况:
- 无监督的图像到图像翻译模型在域名转移方面表现出色.
- 将动漫插图翻译成漫画风格的方法有限.
- 现有的技术与动漫和漫画的独特特征作斗争.
研究的目的:
- 为了调查无监督的图像对图像翻译,动漫转化为漫画风格转换.
- 为了利用动漫和漫画的独特特征来改进翻译.
- 制定一个框架,对目标漫画领域施加最小的限制.
主要方法:
- 提出一个双阶段的翻译过程,利用动漫和漫画的特点.
- 实施一种无监督学习方法,以转移风格.
- 使用参考漫画书来指导翻译.
主要成果:
- 获得了支持翻译过程的初步输出.
- 降低模型复杂性,同时保持高可靠性结果.
- 证明了完全不受监督的域名翻译的潜力.
结论:
- 拟议的方法为动漫-漫画风格翻译提供了一个新的方法.
- 该框架有助于创建合成漫画数据集,用于训练色化模型.
- 这项研究推进了数字艺术和插图的无监督学习应用.
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