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Motion capture for artists using AI apps

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Resumo:Motion capture based on Artificial Intelligence (AI) represents a significant advancement in the capture and manipulation of human motion, providing a more accessible approach to generate lifelike animations. Traditionally, motion capture has been employed to produce realistic character movements for film and video games. However, its applications extend beyond figurative representation, enabling the mapping of motion onto diverse visual forms in various contexts, such as artistic installations and live performances. Traditional motion capture systems are costly, limiting accessibility for artists. AI-driven motion capture has democratized this technology, enabling innovative and abstract visual explorations. A key question arises: do AI-based motion capture tools produce meaningful results in diverse artistic contexts? This paper compares low-cost AI motion capture solutions, to evaluate their potential for artistic applications, through quantitative and qualitative analyses. Motion capture data from these AI solutions were used to generate abstract and non-representational visual interpretations.
Autores principais:Leite, Luis Barbosa
Assunto:Artificial intelligence Motion capture Performance animation Media arts
Ano:2025
País:Portugal
Tipo de documento:artigo
Tipo de acesso:unknown
Instituição associada:Universidade Católica Portuguesa
Idioma:inglês
Origem:Journal of Science and Technology of the Arts
Descrição
Resumo:Motion capture based on Artificial Intelligence (AI) represents a significant advancement in the capture and manipulation of human motion, providing a more accessible approach to generate lifelike animations. Traditionally, motion capture has been employed to produce realistic character movements for film and video games. However, its applications extend beyond figurative representation, enabling the mapping of motion onto diverse visual forms in various contexts, such as artistic installations and live performances. Traditional motion capture systems are costly, limiting accessibility for artists. AI-driven motion capture has democratized this technology, enabling innovative and abstract visual explorations. A key question arises: do AI-based motion capture tools produce meaningful results in diverse artistic contexts? This paper compares low-cost AI motion capture solutions, to evaluate their potential for artistic applications, through quantitative and qualitative analyses. Motion capture data from these AI solutions were used to generate abstract and non-representational visual interpretations.