  Analyses based on the body posture are crucial for top-class athletes in many
sports disciplines. If at all, coaches label only the most important keypoints,
since manual annotations are very costly. This paper proposes a method to
detect arbitrary keypoints on the limbs and skis of professional ski jumpers
that requires a few, only partly correct segmentation masks during training.
Our model is based on the Vision Transformer architecture with a special design
for the input tokens to query for the desired keypoints. Since we use
segmentation masks only to generate ground truth labels for the freely
selectable keypoints, partly correct segmentation masks are sufficient for our
training procedure. Hence, there is no need for costly hand-annotated
segmentation masks. We analyze different training techniques for freely
selected and standard keypoints, including pseudo labels, and show in our
experiments that only a few partly correct segmentation masks are sufficient
for learning to detect arbitrary keypoints on limbs and skis.
