Agree or Disagree? Generating Body Gestures from Affective Contextual Cues during Dyadic Interactions

Humans naturally produce nonverbal signals such as facial expressions, body movements, hand gestures, and tone of voice, along with words, to communicate their messages, opinions, and feelings. Considering robots are progressively moving out from research laboratories into human environments, it is increasingly desirable that they develop a similar social intelligence. Therefore, equipping social robots with nonverbal communication skills has been an active research area for decades, where data-driven, end-to-end learning approaches have become predominant in recent years, offering scalability and generalisability. However, most of these approaches consider a single character, modelling intrapersonal dynamics only. In this paper, we propose a method based on conditional Generative Adversarial Networks, intending to generate behaviours for a robot in affective dyadic interactions. Our method takes as an input the audio of a target person together with the nonverbal signals of their interacting partner, modelled by a novel Context Encoder, to generate appropriate body gestures. We evaluate our method on the multimodal JESTKOD dataset that comprises dyadic interactions under agreement and disagreement scenarios. The experimental results show that Context Encoder can better contribute to the prediction of co-speech gestures in agreement situations.