面部表情变形:在基于CycleGAN的表情合成中增强视觉真实性和保存面部细节
Chayanon Sub-R-Pa1, Rung-Ching Chen1, Ming-Zhong Fan1
1Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan.
PeerJ. Computer science
|December 9, 2024
概括
使用深度学习的面部表情合成现在保留了像子这样的细微面部细节. 我们的面部表情模拟 (FEM) 算法增强了CycleGAN输出,提高了生成面部的图像清晰度和真实性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 像Cycle-Consistent Adversarial Networks (CycleGAN) 这样的深度学习模型在面部表情合成方面表现出色.
- 一个关键的限制是,在生成的图像中,面部细微细节 (如雀斑,) 的丢失.
- 现有的方法在表达转移期间努力保持原始图像的复杂特征.
研究的目的:
- 引入一种新的后处理算法,面部表情模拟 (FEM),用于增强基于深度学习的面部表情合成.
- 为了提高CycleGAN产生的面部表情的视觉真实性和细节保存.
- 用既有和新的指标量化评估拟议方法的有效性.
主要方法:
- 开发了面部表情成形 (FEM) 算法,这是一个后处理技术,将输入图像与合成表情混合在一起.
- 将FEM应用于来自CycleGAN,UNet Vision Transformer循环一致的GAN版本1 (UVCGANv1) 和2 (UVCGANv2) 的输出.
- 在Radboud Faces数据库 (RafD) 上使用Fréchet Inception Distance (FID) 和新提出的面部相似距离 (FSD) 度量来评估性能.
主要成果:
- 在测试模型中,FEM显著提高了图像清晰度,并保留了复杂的面部细节.
- 获得FID平均得分为31.92,比之前的最先进状态 (63.82) 提高了50%.
- FSD指标证实了FEM处理的图像与原始面部之间的更接近相似性,验证了细节的保存.
结论:
- 面部表情变形 (FEM) 算法有效地解决了基于深度学习的面部表情合成中的细节丢失问题.
- 通过保留独特的面部特征,FEM增强了合成表情的现实主义和视觉质量.
- 这项工作在生成高保真,详细的合成面部方面取得了重大进展.
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