Learn2Talk: 3D 谈话面孔从 2D 谈话面孔中学习
IEEE transactions on visualization and computer graphics
|October 7, 2024
概括
Learn2Talk通过整合2D方法来增强口唇同步和感知准确度来增强3D说话面部动画. 这个框架在语音驱动的面部动画中取得了卓越的结果,超过了现有的最先进的技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 语音驱动的面部动画对于逼真的虚拟角色至关重要.
- 现有的3D说话面孔方法在唇同步和感知准确度方面落后于2D对手.
- 弥合这一差距需要新的整合策略.
研究的目的:
- 介绍Learn2Talk,这是一个增强3D说话面孔网络的框架.
- 在3D面部动画中改进唇同步精度和感知口腔运动.
- 为3D应用利用2D谈话面孔研究的见解.
主要方法:
- 开发了一个3Dsync-lip专家模型,灵感来自音视频同步网络.
- 利用2D说话面孔教师模型来指导3D运动回归.
- 将这些组件整合到一个统一的学习框架中.
主要成果:
- 在最先进的方法中表现出优越的性能.
- 在唇同步精度方面取得了显著的改进.
- 展示了增强的顶点精度和感知口腔运动.
结论:
- Learn2Talk有效地弥合了2D和3D说话面孔技术之间的差距.
- 该框架在语音驱动的3D面部动画中提供了增强的现实主义和准确性.
- 应用包括视听语音识别和3D化身动画.
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