Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication

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.

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Publication Details

Published
2025-10-25
DOI
https://doi.org/10.1145/3746027.3758186
Primary Topic
Action Observation and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
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article

Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication

Jordi Sabater-Mir, Holger Sprengel, Esen K. Tütüncü, Lissette Lemus et al.
Action Observation and Synchronization
article

Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication

Jordi Sabater-Mir, Holger Sprengel, Esen K. Tütüncü, Lissette Lemus, Kris Pilcher
article en

Abstract

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.

Artificial Intelligence Research Institute (ES), FC Barcelona (ES), Massachusetts Institute of Technology (US), Universitat de Barcelona (ES)
Openalex Percentile: Top 65%
Action Observation and Synchronization
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Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication — Jordi Sabater-Mir, Holger Sprengel, et al. · (2025) | TGRS Research Map | TGRS