Beyond the Screen With DanceSculpt: A 3D Dancer Reconstruction and Tracking System for Learning Dance

Abstract

Dance learning through online videos has gained popularity, but it presents challenges in providing comprehensive information and personalized feedback. This paper introduces DanceSculpt, a system that utilizes 3D human reconstruction and tracking technology to enhance the dance learning experience. DanceSculpt consists of a dancer viewer that reconstructs dancers in video into 3D avatars and a dance feedback tool that analyzes and compares the user’s performance with that of the reference dancer. We conducted a comparative study to investigate the effectiveness of DanceSculpt against conventional video-based learning. Participants’ dance performances were evaluated using a motion comparison algorithm that measured the temporal and spatial deviation between the users’ and reference dancers’ movements in terms of pose, trajectory, formation, and timing accuracy. Additionally, user experience was assessed through questionnaires and interviews, focusing on aspects such as effectiveness, usefulness, and satisfaction with the system. The results showed that participants using DanceSculpt achieved significant improvements in dance performance compared to those using conventional methods. Furthermore, the participants rated DanceSculpt highly in terms of effectiveness (avg. 4.27) and usefulness (avg. 4.17) for learning dance. The DanceSculpt system demonstrates the potential of leveraging 3D human reconstruction and tracking technology to provide a more informative and interactive dance learning experience. By offering detailed visual information, multiple viewpoints, and quantitative performance feedback, DanceSculpt addresses the limitations of traditional video-based learning and supports learners in effectively analyzing and improving their dance skills.