无调节的自适应式风格集成,用于结构一致的文本驱动式风格转移
IEEE transactions on visualization and computer graphics
|December 5, 2025
概括
本研究介绍了适应式风格整合 (ASI) 用于基于扩散模型的风格转移. ASI增强了风格化图像的内容保存和结构一致性,使艺术家从精确的艺术编辑中受益.
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:
- 使用传播模型的文本驱动风格转移提供了创意潜力,但在内容保存和结构一致性方面存在困难.
- 目前的方法使用粗粒度,提示级别的风格注入,导致风格化图像的不良结构偏差.
- 这种限制阻碍了需要精确控制艺术编辑的专业艺术家.
研究的目的:
- 在文本驱动的风格转移中,在内容保存和风格转换之间实现平衡.
- 引入一种新的细粒度,特征级别风格结合的新方法.
主要方法:
- 拟议的自适应风格整合 (ASI) 方法.
- 开发了语交叉注意力 (SiCA),以分离内容和风格特征.
- 引入了自适应式内容-风格融合 (AdaBlending),以结构一致地将内容和风格结合起来.
主要成果:
- 与现有方法相比,ASI在维护图像结构方面表现出卓越的性能.
- 该方法有效地保存内容,同时实现所需的风格化效果.
- 实验结果验证了SiCA和AdaBlending模块的有效性.
结论:
- 适应式风格整合 (ASI) 在文本驱动的风格转移中提供了显著的进步.
- 提议的细粒度方法解决了结构和内容保存的关键局限性.
- ASI为艺术家提供了精确的艺术编辑和风格转换的增强控制.
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