Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908561040211968 |
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| author | Tütüncü, Esen K. Lemus, Lissette Pilcher, Kris Sprengel, Holger Sabater-Mir, Jordi |
| author_facet | Tütüncü, Esen K. Lemus, Lissette Pilcher, Kris Sprengel, Holger Sabater-Mir, Jordi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_22168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication Tütüncü, Esen K. Lemus, Lissette Pilcher, Kris Sprengel, Holger Sabater-Mir, Jordi Human-Computer Interaction Artificial Intelligence 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. |
| title | Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2509.22168 |