AVLMA : Laban movement analysis as catalyst for generative art in an experimental audiovisual pipeline using mediapipe

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dc.contributor.authorAntunes, Rui Filipe
dc.contributor.authorLima, Cecília de
dc.contributor.editorOlivero, Lucas Fabian
dc.contributor.editorSimao, Emilia
dc.contributor.editorSa, Alberto
dc.contributor.editorFernandes-Marcos, Aderito
dc.contributor.institutionCICANT - Centre for Research in Applied Communication, Culture, and New Technologies
dc.date.accessioned2026-05-12T17:05:03Z
dc.date.available2026-05-12T17:05:03Z
dc.date.issued2026
dc.descriptionPublisher Copyright: © 2025 Copyright held by the owner/author(s).
dc.description.abstractThis paper investigates the potential of real time movement analysis as a driver for generative audiovisual performance. Premised on a generative logic of movement fueling transformation, we propose an experimental pipeline — AVLMA — that fuses Laban Movement Analysis (LMA) with real time motion capture using MediaPipe. The system extracts spatial and qualitative features of bodily movement to modulate generative sound and visuals in a live performance context. Key components include: particle systems that trace bodily landmarks with expressive variation; a swarm based visual model employing boid dynamics to reflect motion complexity; cellular automata that evolve in response to movement flow characteristics; and a FAUST based audio engine responsive to LMA derived parameters. These layers interconnect through a unified interface developed in Unity3D, forming a reactive, self organizing audiovisual ecosystem. Rather than functioning as background embellishment, the audiovisual system becomes an active agent in the experimental performance—interpreting and responding to the dancer’s evolving gestures. This study expands on the notion of choreography as computation and demonstrates how movement analysis can serve not only as input but as an aesthetic grammar for generative mediaen
dc.description.sponsorshipThis article received the support of Fundação para Ciência e a Tecnologia (FCT:CEECINST/00002/2021/CP2788/CT0006); CICANT (reference UIDB/05260/2020); FilmEU - European Universities Alliance for Film and Media Arts; FILMEU_RIT - Research | Innovation | Transformation project, European Union GRANT_NUMBER: H2020-IBA-SwafS Support-2-2020, Ref: 101035820; and FilmEU - The European University for Film and Media Arts project, European Union GRANT_NUMBER: 101004047, EPP-EUR-UNIV-2020
dc.identifier.citationAntunes, R F & Lima, C D 2026, AVLMA : Laban movement analysis as catalyst for generative art in an experimental audiovisual pipeline using mediapipe. in L F Olivero, E Simao, A Sa & A Fernandes-Marcos (eds), ARTECH '25: Proceedings of the 2025 12th International Conference on Digital and Interactive Arts : Media Art Cultures, Communities and Territories., 64, ARTECH 2025 - 12th International Conference on Digital and Interactive Arts: Media Art Cultures, Communities and Territories, Association for Computing Machinery, Inc, 12th International Conference on Digital and Interactive Arts, Braga, Portugal, 26/11/25. https://doi.org/10.1145/3773699.3773703
dc.identifier.doihttps://doi.org/10.1145/3773699.3773703
dc.identifier.isbn979-8-4007-2001-7
dc.identifier.isbn9798400720017
dc.identifier.urihttp://hdl.handle.net/10437/16084
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037866809
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAssociation for Computing Machinery, Inc
dc.publisher12th International Conference on Digital and Interactive Arts
dc.relation.ispartofARTECH '25: Proceedings of the 2025 12th International Conference on Digital and Interactive Arts
dc.relation.ispartofseriesARTECH 2025 - 12th International Conference on Digital and Interactive Arts: Media Art Cultures, Communities and Territories
dc.rightsopenAccess
dc.subjectAUDIOVISUAL
dc.subjectGENERATIVE ART
dc.subjectMOVEMENT
dc.subjectPERFORMANCE
dc.subjectAUDIOVISUAL
dc.subjectARTE GENERATIVA
dc.subjectMOVIMENTO
dc.subjectPERFORMANCE
dc.titleAVLMA : Laban movement analysis as catalyst for generative art in an experimental audiovisual pipeline using mediapipeen
dc.typeconferenceObject

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