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:在许多领域中,实时跟踪 3D 人体运动非常重要。传统方法需要将人工标志物或传感器附加到身体上,限制了它们的可用性和使用舒适度,从而限制了它们的应用领域。最近人工智能领域的进展使无标记解决方案成为了可能。但是,大部分方法仅在2D情况下运行,而提供3D解决方案的方法则牺牲了精度和实时性能。为了应对这一挑战并在真实环境中释放视觉姿态估计方法的潜力,我们提出了一个将多相机视图和基于2D AI的姿态估计方法相结合的无标记框架来跟踪3D人体运动。我们的方法整合了一个加权最小二乘算法,该算法从由AI驱动的2D姿态估计方法提供的多个2D姿态估计中计算3D人体运动。该方法与Open-VICO框架集成,可以进行模拟和实际执行。进行了多项实验,结果表明具有高精度和实时性能,展示了其在真实世界应用方面的高度准备和改变运动捕捉的潜力。