网格WGAN:网格对网格Wasserstein GAN与多任务梯度惩罚3D面部几何年龄转换
IEEE transactions on visualization and computer graphics
|June 12, 2023
概括
本研究介绍了MeshWGAN,这是一个新的生成对抗网络,用于使用真实3D扫描进行3D面部年龄转换. 它可以为诸如元宇宙化身和数据增强等应用程序实现现实的几何老化.
科学领域:
- 计算机图形和视觉视觉
- 人工智能的人工智能
- 3D几何建模 3D几何建模
背景情况:
- 3D面部年龄转换对于metaverse应用至关重要,但与2D方法相比,仍未得到充分探索.
- 现有的2D图像对图像翻译技术不适合3D面部网格数据.
- 对年轻年龄组 (5-17岁) 显著缺乏3D数据集,这阻碍了研究.
研究的目的:
- 通过使用真实3D扫描,提出第一个用于3D面部几何年龄转换的架构.
- 为了建模一个连续的,双向的3D面部几何老化过程.
- 为了解决现有的3D网状数据和数据集稀缺性方法的局限性.
主要方法:
- 开发了一个网格对网格瓦斯斯坦生成对抗网络 (MeshWGAN),具有多任务梯度惩罚.
- 设计了一个针对3D网格到网格转换量身定制的网格编码器,解码器和多任务区分器.
- 收集并将765名受试者 (5-17岁) 的3D扫描与现有数据库结合起来,以创建一个大型的培训数据集.
主要成果:
- 拟议的MeshWGAN架构在真实3D扫描上成功执行了3D面部几何年龄转换.
- 与基线3D方法相比,实现了优越的身份保护和年龄接近.
- 在各种3D面部相关图形应用中证明了有效性.
结论:
- MeshWGAN代表了面部3D年龄转换的重大进步,可以实现现实的几何老化.
- 该方法克服了与3D网状数据和数据集限制相关的挑战.
- 该方法有望增强元宇宙内容创作,3D面部数据增强和编辑.
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