Believable and Manipulable Facial Behaviour in a Robotic Platform using Normalizing Flows

Sammanfattning: Implicit communication is important in interaction because it plays a role in conveying the internal mental states of an individual. For example, emotional expressions that are shown through unintended facial gestures can communicate underlying affective states. People can infer mental states from implicit cues and have strong expectations of what those cues mean. This is true for human-human interactions, as well as human-robot interactions. A Normalizing flow model is used as a generative model that can produce facial gestures and head movements. The invertible nature of the Normalizing flow model makes it possible to manipulate attributes of the generated gestures. The model in this work is capable of generating facial expressions that look real and human-like. Furthermore, the model can manipulate the generated output to change the perceived affective state of the facial expressions.