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戈亚:利用生成艺术来实现内容-风格脱而出
Yankun Wu1, Yuta Nakashima1, Noa Garcia1
1Intelligence and Sensing Lab, Osaka University, Suita 565-0871, Osaka, Japan.
Journal of imaging
|July 26, 2024
概括
我们介绍GOYA,一种使用生成模型将艺术内容与视觉风格分开的新方法. 这种方法有效地捕捉了这两个维度,推进了艺术分析.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 艺术史 艺术史 艺术史
背景情况:
- 内容-风格二元性是艺术的关键,内容是指对象/概念,风格是指视觉外观.
- 人类很容易区分内容和风格,但计算方法难以有效地捕捉这种二元性.
- 现有的方法,如风格转移缺乏一般化,分类是不切实际的,因为风格的频谱.
研究的目的:
- 开发一种计算方法来解开艺术作品中的内容和风格.
- 利用尖端的生成模型来捕捉艺术知识.
- 通过分离其组成的维度,使艺术能够进行更细微的分析.
主要方法:
- 介绍GOYA,一个旨在从生成模型中捕获艺术知识的系统.
- 使用生成模型来明确学习和表示不同的艺术维度.
- 开发一种新的方法来解开视觉艺术中的内容和风格.
主要成果:
- GOYA成功地学会了艺术品的内容和风格维度.
- 该方法展示了艺术内容和风格的明确解.
- 实验结果验证了GOYA在捕捉内容风格二元性的能力.
结论:
- GOYA提供了一个强大的框架来分离艺术中的内容和风格.
- 这项工作为在艺术分析中利用生成模型铺平了道路.
- 这些发现为计算艺术研究和理解艺术表现提供了新的可能性.
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