Exploring the impact of machine learning on dance performance: a systematic review

Abstract

ABSTRACT In recent years, the intersection of machine learning and dance has garnered increasing attention as a means to enhance the aesthetics and creativity of dance performance. Machine learning techniques, such as human-pose detection, have been utilized to analyze body movement and generate visual models in realtime, offering new approaches to traditional art form. However, despite the growing interest in this field, there is a lack of comprehensive research that evaluates the impact of machine learning on dance from multiple perspectives. This systematic review addresses this gap by examining the impact of machine learning on four key aspects of dance performance: choreographic creation support; dataset or network collection and training; improved techniques of human-pose detection; perception and new visual representations of dance movement. This exploration reveals that machine learning contributes both positively and negatively to artistic development, introducing a sense of novelty and heightened interest in the performance while concurrently prompting ethical concerns regarding artistic ownership and authorship. Through a nuanced analysis of machine learning's effects on key aspects of dance, this study aims to provide valuable insights into how these technologies can enhance the artistic practice in dance while signaling areas that demand further research in this rapidly evolving field.