Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture.

翻译：实时追踪三维人体运动对于众多领域的广泛应用至关重要。传统方法需在身体上附着人工标记物或传感器，这限制了其可用性与舒适性，进而缩小了应用范围。近年来人工智能（AI）的进步使无标记解决方案成为可能，然而多数此类方法仅能处理二维姿态，而提供三维解的方案则在精度与实时性能上有所妥协。为应对这一挑战，并释放视觉姿态估计方法在现实场景中的潜力，我们提出了一种无标记框架，该框架融合多摄像机视角与基于AI的二维姿态估计方法，用于追踪三维人体运动。该方法整合了加权最小二乘算法，可从AI驱动的二维姿态估计结果中计算三维人体运动，并集成至Open-VICO框架中，支持仿真及实际执行。通过多项实验验证，该方法展现出高精度与实时性能，充分表明其已具备应对现实应用的高度成熟性，并具有革新人体运动捕捉技术的潜力。