  In sports, such as alpine skiing, coaches would like to know the speed and
various biomechanical variables of their athletes and competitors. Existing
methods use either body-worn sensors, which are cumbersome to setup, or manual
image annotation, which is time consuming. We propose a method for estimating
an athlete's global 3D position and articulated pose using multiple cameras. By
contrast to classical markerless motion capture solutions, we allow cameras to
rotate freely so that large capture volumes can be covered. In a first step,
tight crops around the skier are predicted and fed to a 2D pose estimator
network. The 3D pose is then reconstructed using a bundle adjustment method.
Key to our solution is the rotation estimation of Pan-Tilt cameras in a joint
optimization with the athlete pose and conditioning on relative background
motion computed with feature tracking. Furthermore, we created a new alpine
skiing dataset and annotated it with 2D pose labels, to overcome shortcomings
of existing ones. Our method estimates accurate global 3D poses from images
only and provides coaches with an automatic and fast tool for measuring and
improving an athlete's performance.
