Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions
This work addresses the need for more human-like interactions in virtual reality and avatar-mediated applications, such as tutoring and e-learning systems, to promote positive effects, but it is incremental as it builds on existing generative adversarial network approaches.
The paper tackles the problem of generating natural facial behaviors for virtual agents in dyadic interactions by proposing a method that learns semantically meaningful representations to produce appropriate and temporally smooth facial expressions and head poses based on the user's affective state.
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.