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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Expressive Facial Animation Synthesis by Learning Speech Coarticulation and Expression Spaces
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Expressive Facial Animation Synthesis by Learning Speech Coarticulation and Expression Spaces

机译：通过学习语音发音和表达空间表达表情动画

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摘要

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns" speech coarticulation models for diphones and triphones from the recorded data. A phoneme-independent expression eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and principal component analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated to synthesize neutral visual speech according to novel speech input, then a texture-synthesis-based approach is used to generate a novel dynamic expression signal from the PIEES model, and finally the synthesized expression signal is blended with the synthesized neutral visual speech to create the final expressive facial animation. Our experiments demonstrate that the system can effectively synthesize realistic expressive facial animation
机译：合成表情面部动画是图形社区中一个非常具有挑战性的主题。在本文中，我们介绍了一种具有表现力的面部动画合成系统，该系统可以通过从面部运动捕获数据中自动学习来实现。当他/她背诵带有特定口头和视觉表达的预先设计的语料库时，可以捕获标记在对象面部上的准确3D运动。我们提出了一种新颖的运动捕捉挖掘技术，该技术可从记录的数据中“学习”针对双音和三音的语音共鸣模型。通过运动信号处理（基于音素的时间扭曲和减法）和主成分分析（PCA）缩减，构造了包围动态表达信号的独立于音素的表达本征空间（PIEES）。新的表情面部动画合成方法如下：首先，将学习的共发音模型连接起来，根据新颖的语音输入来合成中性的视觉语音，然后使用基于纹理合成的方法从PIEES模型生成新颖的动态表达信号，最后，将合成的表情信号与合成的中性视觉语音混合，以创建最终的表情面部动画。我们的实验表明，该系统可以有效地合成逼真的表情面部动画

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