图像对图像翻译使用解的潜向量来进行面部编辑
IEEE transactions on pattern analysis and machine intelligence
|August 24, 2023
概括
这项研究引入了一个新的框架,用于面部属性编辑使用脱的隐藏方向. 该方法使图像中的属性操纵能够得到控制和解,大大改善了现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部属性编辑需要对特定属性进行有针对性的控制,同时保留其他属性.
- 现有的图像对图像翻译方法在解和可控制的编辑强度方面扎.
研究的目的:
- 开发面部属性编辑的图像对图像翻译框架.
- 为了实现可控制属性操纵的解和可解释的潜在方向.
- 为了保留面部属性而不是针对编辑.
主要方法:
- 潜在空间因子化灵感来自预训练的生成对抗网络 (GANs).
- 学习每个属性的直角线性方向,具有直角性约束和解损失.
- 使用编码器-解码器架构与基于注意力的跳过连接用于图像投影.
主要成果:
- 提出的方法显示了与最先进的图像翻译和GAN编辑技术相比的显著改进.
- 在面部属性编辑中实现可控制的强度和解.
- 在操作过程中有效保存未经编辑的属性.
结论:
- 该框架提供了一个强大的解决方案,用于解和可控的面部属性编辑.
- 潜在空间因子化为可解释图像操纵提供了一种有效的方法.
- 该方法推进了用于语义编辑任务的图像对图像翻译领域.
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