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Tracking 3D human motion in real-time is crucial for numerous applicationsacross many fields. Traditional approaches involve attaching artificialfiducial objects or sensors to the body, limiting their usability andcomfort-of-use and consequently narrowing their application fields. Recentadvances in Artificial Intelligence (AI) have allowed for markerless solutions.However, most of these methods operate in 2D, while those providing 3Dsolutions compromise accuracy and real-time performance. To address thischallenge and unlock the potential of visual pose estimation methods inreal-world scenarios, we propose a markerless framework that combinesmulti-camera views and 2D AI-based pose estimation methods to track 3D humanmotion. Our approach integrates a Weighted Least Square (WLS) algorithm thatcomputes 3D human motion from multiple 2D pose estimations provided by anAI-driven method. The method is integrated within the Open-VICO frameworkallowing simulation and real-world execution. Several experiments have beenconducted, which have shown high accuracy and real-time performance,demonstrating the high level of readiness for real-world applications and thepotential to revolutionize human motion capture.

Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance