arXiv 1801.090922018-01-30cs.CV

基于交互式生成对抗网络的二元交互人脸表情生成

Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions

University of Central Florida（中佛罗里达大学）；Educational Testing Service（教育考试服务中心）

Behnaz Nojavanasghari, Yuchi Huang, Saad Khan

摘要：

社交互动是两个或多个个体之间的社会交换，其中个体修改和调整其行为以响应其互动伙伴。我们的社会交往是我们生活中最基本的方面之一，可以深刻地影响我们的情绪，无论是积极的还是消极的。随着对虚拟现实和化身介导的交互的兴趣的增长，期望使这些交互自然并且像人类一样以促进交互和应用（诸如智能辅导系统、自动面试系统和电子学习）中的积极效果。在本文中，我们提出了一种方法来产生面部行为的代理。这些行为包括面部表情和头部姿势，它们是考虑用户的情感状态而生成的。我们的模型学习语义上有意义的面部表示，并在二元交互中生成适当的和时间上平滑的面部行为。

英文摘要：

A social interaction is a social exchange between two or more individuals,where individuals modify and adjust their behaviors in response to their interaction partners. Our social interactions are one of most fundamental aspects of our lives and can profoundly affect our mood, both positively and negatively. With growing interest in virtual reality and avatar-mediated interactions,it is desirable to make these interactions natural and human like to promote positive effect in the interactions and applications such as intelligent tutoring systems, automated interview systems and e-learning. In this paper, we propose a method to generate facial behaviors for an agent. These behaviors include facial expressions and head pose and they are generated considering the users affective state. Our models learn semantically meaningful representations of the face and generate appropriate and temporally smooth facial behaviors in dyadic interactions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

相关论文