GAN-AVI:面部表情翻译器在Twitter的化身类似于推文情绪
Kunada Dhana Sree Devi1, Kireet Muppavaram1, Choudapur Atheeq1
1Department of CSE, GSOT, GITAM (Deemed University), Hyderabad, Telangana, India.
Scientific reports
|October 16, 2025
概括
这项研究介绍了GAN-AVI,这是一种深度学习模型,可以动态调整推特化身的面部表情,以匹配推特情绪,增强道德沟通和用户感知. 它改进了情感表达对齐,以便更准确地生成微观表达.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 像Twitter (X) 这样的社交媒体平台被广泛用于表达意见,但用户化身 (AVI) 往往与推特情绪不匹配.
- 这种不匹配可能会导致道德问题,并改变对推特者的真诚的看法.
研究的目的:
- 提出一个名为GAN-AVI的深度学习生成对抗网络 (GAN) 模型,用于动态翻译AVI面部表情以与推特情绪保持一致.
- 提高在线沟通中表达的情绪和视觉表现之间的伦理一致性.
主要方法:
- 开发了一个双框架架构:推特情绪标签提取和目标面部合成.
- 在10,000条推特上训练了情绪框架,在35,000张图像上训练了面部合成框架,以提取32个面部地标.
- 整合了从情感文本中取决于时间词的依赖,使用掩饰嵌入来改善情感表达对齐.
主要成果:
- 通过使用AED,SSIM,LMK,ID和NCC等指标评估了GAN-AVI.
- 在微表达式生成的地标本地化中实现了0.2%的最低错误率.
- 与基线模型 (如GANimation,X2Face,AttentionGAN和C2GAN) 相比,表现出更高的性能.
结论:
- GAN-AVI有效地合成了动态匹配推特情绪的AVI面部表情.
- 该模型增强了情感表达对齐,在社交媒体上提供了更符合伦理和准确的表现.
- 拟议的方法在微表达式生成和地标定位精度方面取得了重大进展.
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