We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matching module that predicts the alignment among each sensor, even in the absence of calibration. Additionally, our coarse-to-fine sensor-expandable pose optimizer further optimizes the 3D human key points and the alignments, it is also capable of incorporating additional cameras to enhance accuracy. Extensive experiments on Human-M3 and FreeMotion datasets demonstrate that our method significantly outperforms state-of-the-art single-modal methods, offering an expandable and efficient solution for multi-person motion capture across various applications.

翻译：我们提出了一种新颖的混合免标定方法 FreeCap，用于在开放环境中精确捕捉全局多人运动。我们的系统将单个 LiDAR 与可扩展的移动相机相结合，从而能够在统一的世界坐标系中进行灵活且精确的运动估计。具体而言，我们引入了一个局部到全局的姿态感知跨传感器人体匹配模块，该模块即使在缺乏标定的情况下也能预测各传感器之间的对齐关系。此外，我们提出的由粗到精、可扩展传感器的姿态优化器进一步优化了三维人体关键点和对齐关系，并且能够融合额外的相机以提升精度。在 Human-M3 和 FreeMotion 数据集上进行的大量实验表明，我们的方法显著优于当前最先进的单模态方法，为跨多种应用的多人运动捕捉提供了一个可扩展且高效的解决方案。