CGOF++:可控制的3D面部合成与条件生成占用场
IEEE transactions on pattern analysis and machine intelligence
|December 28, 2023
概括
这项研究引入了一个新的3D面部合成框架,使用神经辐射场 (NeRF) 进行精确的3D控制. 它生成高保真面部图像,在表情和姿势变化中提高一致性.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 现有的2D可控面部合成方法在显著的姿势和表情变化下难以保持一致.
- 图像生成模型的近期进展使高保真面部合成具有一定可控性.
研究的目的:
- 提出一种基于神经辐射场 (NeRF) 的新框架,用于可控制的3D面部合成.
- 通过整合来自3D面部先验的显式3D条件来增强3D可控性.
主要方法:
- 在EG3D生成模型基础上开发了一个有条件的生成占用场 (cGOF++).
- 包含一个3D可变形模型 (3DMM) 网格,以强制执行面部形状的一致性.
- 集成的3D地标损失和体积扭曲损失,用于精细粒度的3D形状控制.
主要成果:
- 拟议的框架可以生成高保真度的3D面部图像.
- 与最新的基于2D的方法相比,实现了更精确的3D可控性.
- 在大表情和姿势变化下,在生成的面部图像中表现出更好的一致性.
结论:
- 基于NeRF的条件3D面部合成框架提供了卓越的3D可控性.
- 该方法有效地解决了面部合成2D生成模型的局限性.
- 该框架可以精确控制细粒度的3D面部形状.
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