Advances in Markerless Motion Capture Systems: A Review of OpenCap and Its Applications

Abstract

Motion capture technology has long been a cornerstone of biomechanical research and analysis, traditionally relying on marker-based systems. However, these systems have inherent limitations, including high costs, time-consuming setup, and constraints on natural movement. Recent advancements in computer vision and machine learning have paved the way for markerless motion capture systems, such as OpenCap, which offer a more accessible and natural approach to biomechanical analysis. This review focuses on OpenCap, exploring its accuracy, challenges, and future potential, while comparing it with traditional motion capture technologies and discussing its applications in sports, clinical rehabilitation, and everyday use. Areas in need of refinement, such as improving pose estimation algorithms and addressing inter-trial variability, are identified as key future research directions. Other markerless systems are also compared in terms of advantages, limitations, and application. The findings suggest that while OpenCap and similar systems hold significant promise, further research and refinement are necessary to fully realize their potential and integrate them seamlessly into various biomechanical applications.