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从单个2D图像进行高级3D面部重建,使用增强的对抗神经网络和图形神经网络

Mohamed Fathallah¹, Sherif Eletriby², Maazen Alsabaan³

¹Department of Computer Science, Faculty of Computers and Information, Kafr El-Sheikh University, Kafr El-Sheikh 33511, Egypt.

Sensors (Basel, Switzerland)

|October 16, 2024

概括

这项研究引入了使用图形神经网络从2D图像进行3D面部重建的新框架. 这种新的方法提高了准确性和稳定性,比现有方法显著提高了性能.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
3D 图形 3D 图形

背景情况:

现有的3D面部重建方法在准确性和稳健性方面扎.
基于单个2D图像的重建,由于固有的深度模两可,提出了重大挑战.

研究的目的:

从单个2D图像开发一个新的框架,用于准确和强大的3D面部重建.
解决现有方法的局限性,特别是模式崩和生成模型的不稳定性.

主要方法:

修改的对抗性神经网络与图形神经网络 (GCN) 的集成.
使用基于GCN的发电机,具有新的损失函数和身份块.
集成的面部地标和一个高效的网络解码器.
采用轻量级的基于GCN的歧视器.

主要成果:

在300W-LP和AFLW2000-3D数据集上实现了最先进的性能.
在300W-LP.上减少了62.7%的手距离和57.1%的地球移动距离.
在姿势,遮蔽,噪音和照明的变化中表现出优越的坚固性.

结论:

拟议的框架为3D面部重建的准确性和稳定性提供了显著的改进.
GCN和对抗网络的新集成为现实世界的场景提供了强大的解决方案.
与现有技术相比,该方法实现了更快的处理时间.

关键词:

3D重建重建的3D重建这是一把手枪.全国CN的 GCNs 是什么?有效的网络.

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