语音驱动的面部合成从3D视频

I. A. Ypsilos, A. Hilton, A. Turkmani, P. Jackson
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引用次数: 14

摘要

我们提出了一个从一个人说话的3D视频语料库中合成真实面孔的语音驱动框架。动态三维人脸形状和颜色外观的视频速率捕获为视觉语音合成模型提供了基础。位移图表示将面部形状和颜色结合到3D视频中。这种表示用于有效地注册和整合从多个视图捕获的形状和颜色信息。为了实现视觉语音合成,使用自动语音识别技术从语料库中识别出视素原语。提出了一种新的非刚性对齐算法,用于估计不同视位的三维人脸形状和外观之间的密集对应关系。注册的位移图表示以及使用形状和颜色的新型光流优化,实现了准确有效的非刚性对齐。语音人脸合成是通过使用非刚性对应的相应视素序列进行拼接来重现3D人脸形状和颜色外观。串联合成再现了粘粒计时和协同发音。人脸捕捉和合成已经对51人的数据库进行了。结果表明,合成的3D视觉语音动画的质量可与捕获的人的视频相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speech-driven face synthesis from 3D video
We present a framework for speech-driven synthesis of real faces from a corpus of 3D video of a person speaking. Video-rate capture of dynamic 3D face shape and colour appearance provides the basis for a visual speech synthesis model. A displacement map representation combines face shape and colour into a 3D video. This representation is used to efficiently register and integrate shape and colour information captured from multiple views. To allow visual speech synthesis viseme primitives are identified from the corpus using automatic speech recognition. A novel nonrigid alignment algorithm is introduced to estimate dense correspondence between 3D face shape and appearance for different visemes. The registered displacement map representation together with a novel optical flow optimisation using both shape and colour, enables accurate and efficient nonrigid alignment. Face synthesis from speech is performed by concatenation of the corresponding viseme sequence using the nonrigid correspondence to reproduce both 3D face shape and colour appearance. Concatenative synthesis reproduces both viseme timing and co-articulation. Face capture and synthesis has been performed for a database of 51 people. Results demonstrate synthesis of 3D visual speech animation with a quality comparable to the captured video of a person.
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