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A social interaction is a social exchange between two or moreindividuals,where individuals modify and adjust their behaviors in response totheir interaction partners. Our social interactions are one of most fundamentalaspects of our lives and can profoundly affect our mood, both positively andnegatively. With growing interest in virtual reality and avatar-mediatedinteractions,it is desirable to make these interactions natural and human liketo promote positive effect in the interactions and applications such asintelligent tutoring systems, automated interview systems and e-learning. Inthis paper, we propose a method to generate facial behaviors for an agent.These behaviors include facial expressions and head pose and they are generatedconsidering the users affective state. Our models learn semantically meaningfulrepresentations of the face and generate appropriate and temporally smoothfacial behaviors in dyadic interactions.

Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions