The filming of sporting events projects and flattens the movement of athletes in the world onto a 2D broadcast image. The pixel locations of joints in these images can be detected with high validity. Recovering the actual 3D movement of the limbs (kinematics) of the athletes requires lifting these 2D pixel locations back into a third dimension, implying a certain scene geometry. The well-known line markings of sports fields allow for the calibration of the camera and for determining the actual geometry of the scene. Close-up shots of athletes are required to extract detailed kinematics, which in turn obfuscates the pertinent field markers for camera calibration. We suggest partial sports field registration, which determines a set of scene-consistent camera calibrations up to a single degree of freedom. Through joint optimization of 3D pose estimation and camera calibration, we demonstrate the successful extraction of 3D running kinematics on a 400m track. In this work, we combine advances in 2D human pose estimation and camera calibration via partial sports field registration to demonstrate an avenue for collecting valid large-scale kinematic datasets. We generate a synthetic dataset of more than 10k images in Unreal Engine 5 with different viewpoints, running styles, and body types, to show the limitations of existing monocular 3D HPE methods. Synthetic data and code are available at https://github.com/tobibaum/PartialSportsFieldReg_3DHPE.

翻译：体育赛事拍摄将运动员在世界坐标系中的运动投影并压缩至2D转播图像中。这些图像中关节的像素位置可被高精度检测。要将运动员肢体的实际3D运动（运动学）还原，需要将这些2D像素位置提升至第三维度，这必然涉及特定的场景几何结构。体育场地上众所周知的标线可用于相机标定并确定实际场景几何。获取精细运动学需要运动员特写镜头，但这会遮挡用于相机标定的关键场地标记。我们提出部分体育场地配准方法，该方法可确定一组仅剩单一自由度的场景一致性相机标定参数。通过3D姿态估计与相机标定的联合优化，我们成功在400米跑道上提取了跑步运动的3D运动学数据。本研究融合了2D人体姿态估计与通过部分体育场地配准实现的相机标定技术进展，为采集大规模有效运动学数据集开辟了新途径。我们在Unreal Engine 5中生成包含不同视角、跑步风格和体型特征的超过10,000张图像的合成数据集，以揭示现有单目3D HPE方法的局限性。合成数据与代码见https://github.com/tobibaum/PartialSportsFieldReg_3DHPE。