Publicação
LMA driven dynamic audiovisuals in a virtual reality live dance performance: Ghostdance
| Resumo: | Ghostdance is an evolving generative art project in the field of dance and virtual reality (VR). It mixes visual, auditory and immersive experiences, making use of generative algorithms to create a dynamic audiovisual landscape with continuously changing images and sounds. The performance consists of three interconnected components: a) a duet featuring a human dancer and an avatar mirroring the movements of an absent person; b) the transformation of the physical movements of the human dancer into a visualization of a hybrid body, constantly redefined as a swarm of virtual entities; and c) the sonification of the dancer’s movements, introducing an auditory dimension to the exploration of movement. Performers dance in duets with virtual bodies, with prechoreographed movements, in a visual and auditory landscape that evolves in real time due to adaptive generative algorithms responding to the presence and movements of the performer, informed by pretrained machine learning algorithms able to categorize the quality of the dancer’s movement in Laban terms. |
|---|---|
| Autores principais: | Lima, Cecília de |
| Assunto: | Virtual reality Dance Live performance Evolutionary audiovisuals |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | Ghostdance is an evolving generative art project in the field of dance and virtual reality (VR). It mixes visual, auditory and immersive experiences, making use of generative algorithms to create a dynamic audiovisual landscape with continuously changing images and sounds. The performance consists of three interconnected components: a) a duet featuring a human dancer and an avatar mirroring the movements of an absent person; b) the transformation of the physical movements of the human dancer into a visualization of a hybrid body, constantly redefined as a swarm of virtual entities; and c) the sonification of the dancer’s movements, introducing an auditory dimension to the exploration of movement. Performers dance in duets with virtual bodies, with prechoreographed movements, in a visual and auditory landscape that evolves in real time due to adaptive generative algorithms responding to the presence and movements of the performer, informed by pretrained machine learning algorithms able to categorize the quality of the dancer’s movement in Laban terms. |
|---|