"FORMS" - Creating new visual perceptions of dance movement through machine learning

"FORMS" is a new digital art concept that combines the fields of dance movement and machine learning techniques, specifically human pose detection, to create a real-time and interactive visual experience. This project aims to explore the relationship between dance and visual art by creating a framework that generates abstract and literal visual models from the dancers' movements. The main objective of this project is to enhance the perception of dance movement by providing a new layer of visual composition. The proposed framework provides different visual forms based on human pose detection, creating a novel and real-time visual expression of the dance movement. The human pose detection model used in this project is based on state-of-the-art deep learning techniques, which analyze the positions and movements of different parts of the human body in real-time. This model allows the framework to capture movements of the dancers and translate them into unique visual forms. The case study showcases the potential of "FORMS" by demonstrating how professional young dancers can use the framework to enrich their performance and create new visual perceptions of dance movement. This study contributes to the cultivation of body awareness, understanding of the dance movement and overall enrichment of the art experience. The use of machine learning techniques showcases the potential of technology to enhance and expand the boundaries of artistic expression. The "FORMS" project is a novel and interdisciplinary approach that bridges the fields of art and technology, providing a new way to experience and perceive the dance movement.