绘画风格转移与学习的笔触
IEEE transactions on visualization and computer graphics
|November 15, 2023
概括
这项研究引入了一种基于神经风格转移 (NST) 的创新性脑卒中方法,通过分析和采样艺术脑卒中家族来产生更具绘画意义的图像. 它还通过语言图像模型来提高输出质量来加强突出内容的强调.
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:
- 传统的神经风格转移 (NST) 通常使用纹理补丁,导致输出缺乏绘画美学.
- 现有的NST方法很难复制现实世界绘画中特有的微妙的笔触应用.
- 内容的语义意义和视觉突出性没有被传统的NST方法充分捕获.
研究的目的:
- 开发一种基于冲击的神经风格转移 (NST) 的新方法,产生更具绘画性和美学意义的结果.
- 从现实世界绘画中分析和建模艺术笔画家族.
- 通过整合语言-图像模型来改善风格化图像中突出语义内容的强调.
主要方法:
- 对现实世界的绘画进行分析,以学习基于形状 (点,线,弧) 的中风家族分布.
- 在合成过程中采样学习的冲击分布,以确保造型使用适当的冲击类型.
- 开发基于语言图像模型的新内容丢失函数,以优先考虑语义上有意义和突出的图像区域.
主要成果:
- 拟议的基于冲击的NST方法产生了与基于纹理的NST相比明显更具绘画性的输出.
- 学习的中风家族被有效地抽取样本来指导风格化过程,提高艺术品质.
- 使用基于语言图像模型的损失函数成功强调突出的图像内容,提高输出保真度.
结论:
- 基于冲击的方法比基于纹理的方法提供了显著的改进,以实现绘画的神经风格转移.
- 模拟和采样艺术创作家族对于产生真实的艺术风格至关重要.
- 通过语言图像模型整合语义理解,通过强调突出内容来提高风格化的图像的质量和焦点.
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