In this paper, we propose RPGD (RANSAC-P3P Gradient Descent), a human-pose-driven extrinsic calibration framework that robustly aligns MoCap-based 3D skeletal data with monocular or multi-view RGB cameras using only natural human motion. RPGD formulates extrinsic calibration as a coarse-to-fine problem tailored to human poses, combining the global robustness of RANSAC-P3P with Gradient-Descent-based refinement. We evaluate RPGD on three large-scale public 3D HPE datasets as well as on a self-collected in-the-wild dataset. Experimental results demonstrate that RPGD consistently recovers extrinsic parameters with accuracy comparable to the provided ground truth, achieving sub-pixel MPJPE reprojection error even in challenging, noisy settings. These results indicate that RPGD provides a practical and automatic solution for reliable extrinsic calibration of large-scale 3D HPE dataset collection.

翻译：本文提出RPGD（RANSAC-P3P梯度下降）——一种基于人体姿态驱动的外参标定框架，该框架仅利用自然人体运动即可鲁棒地将基于动作捕捉的三维骨骼数据与单目或多视角RGB相机对齐。RPGD将外参标定构建为适应人体姿态特点的由粗到精优化问题，结合了RANSAC-P3P的全局鲁棒性与基于梯度下降的精细化调整。我们在三个大规模公开三维人体姿态估计数据集及自采集的真实场景数据集上对RPGD进行评估。实验结果表明，RPGD能够稳定恢复与标注真值精度相当的外参，即使在具有挑战性的噪声场景中也能实现亚像素级MPJPE重投影误差。这些结果证明RPGD为大规模三维人体姿态估计数据集采集提供了实用且自动化的可靠外参标定解决方案。