Machine Art: Exploring Abstract Human Animation Through Machine Learning Methods.

Visual media and performance art have a symbiotic relationship. They support one another and engage the audience by providing an experience or telling a story. This comparative study explores the accuracy, efficiency, and cost factors of using machine learning based motion capture methods in performance art. There is extensive research in the field of machine learning methods for human pose estimation, but the outputs of such work are rarely used as inputs for performance art. In this paper we present a practice-based research project that involves producing animations that match a performer's movements using machine learning based motion capture methods. We use human poses derived from low-cost video capture as an input into high-resolution abstract forms that accompany and synchronise with dance performances. A single-camera approach is examined and compared to existing methods. We find that compared with existing motion capture methods the machine learning based methods require less setup time, and less equipment is required resulting in considerably lower cost. This research suggests that machine learning has considerable potential to improve the quality of human pose estimation in performance art, visual effects and motion capture, and make it more accessible for arts companies with limited resources.