Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication
This work addresses the need for more inclusive and ethical affective computing in multimedia research, moving beyond single-user facial analysis to an embodied, co-created paradigm.
The paper tackles the problem of emotion recognition in AI by shifting from top-down classification to a participant-driven, collaborative approach using full-body motion tracking and real-time feedback, resulting in an interactive installation that fosters user agency and reduces bias.
Commonaiverse is an interactive installation exploring human emotions through full-body motion tracking and real-time AI feedback. Participants engage in three phases: Teaching, Exploration and the Cosmos Phase, collaboratively expressing and interpreting emotions with the system. The installation integrates MoveNet for precise motion tracking and a multi-recommender AI system to analyze emotional states dynamically, responding with adaptive audiovisual outputs. By shifting from top-down emotion classification to participant-driven, culturally diverse definitions, we highlight new pathways for inclusive, ethical affective computing. We discuss how this collaborative, out-of-the-box approach pushes multimedia research beyond single-user facial analysis toward a more embodied, co-created paradigm of emotional AI. Furthermore, we reflect on how this reimagined framework fosters user agency, reduces bias, and opens avenues for advanced interactive applications.