  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.
