Facial expression generation of 3D avatar based on semantic analysis
Facial expression generation of 3D avatar based on semantic analysis
复制标题
基于语义分析的3D头像面部表情生成
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Sandygulova
中科院分区：
文献类型：
--
作者：
Dinmukhamed Mukashev;Merey Kairgaliyev;Ulugbek Alibekov;Nurziya Oralbayeva;A. Sandygulova
3D化身广泛应用于新兴技术的各个领域，从增强现实到社交机器人。为了以自然的方式与用户交互，他们必须能够表现出至少一些基本的情感。然而，为这些虚拟化身生成动画是一项耗时的任务和创造性的过程。这项工作的主要目标是促进生成的面部动画的基本情绪的3D化身。为此，我们开发并比较了两种方法。第一种方法包括使用调整3D模型的Blendshape特征来生成动画，而第二种方法从真实的面部捕获动画并将其相应地映射到模型上。此外，从中估计情感的文本被传递到嘴唇同步软件，用于为化身生成逼真的嘴唇运动。然后，六种基本情绪的动画在调查中以不同的变化显示，受访者被要求猜测视频中显示的情绪。此外，还考察了化身的拟人性、逼真性和愉悦性等特征。总的来说，对调查的分析提供了以下有趣的发现：a）参与者在基于动画生成方法的类型识别情感方面没有显著差异; B）包括语音显着增强了对情感的识别。在情绪识别的准确性方面，兴奋和快乐最容易相互混淆，而愤怒是最容易识别的情绪。
3D avatars are widely used in various fields of emerging technology, from augmented reality to social robots. To interact in a natural way with the user, they must be able to show at least some basic emotions. However, generating animation for these virtual avatars is a time-consuming task and a creative process. The main goal of this work is to facilitate a generation of facial animation of basic emotions on a 3D avatar. To this end, we developed and compared two approaches. The first method consists of the generation of animation using tuning Blendshape features of the 3D model, whereas the second method captures it from the real face and maps it on the model correspondingly. Additionally, the text, from which the emotion was estimated, was passed to lip synchronization software for generating realistic lip movements for the avatar. Then, animations of six basic emotions were shown in different variations in the survey and respondents were asked to guess the emotion shown in the video. Besides, such anthropomorphic features of the avatar as human-likeness, life-likeness and pleasantness were examined. In general, the analysis of the survey provided the following interesting findings: a) participants did not have significant differences in recognizing emotions based on the type of animation generation method; b) inclusion of voice significantly enhanced the recognition of emotion. In relation to participants’ accuracy of emotion recognition, Excitement and Happiness were mostly confused between each other more than any other two emotions, while Anger was the easiest emotion to recognize.