  Human pose is a useful feature for fine-grained sports action understanding.
However, pose estimators are often unreliable when run on sports video due to
domain shift and factors such as motion blur and occlusions. This leads to poor
accuracy when downstream tasks, such as action recognition, depend on pose.
End-to-end learning circumvents pose, but requires more labels to generalize.
  We introduce Video Pose Distillation (VPD), a weakly-supervised technique to
learn features for new video domains, such as individual sports that challenge
pose estimation. Under VPD, a student network learns to extract robust pose
features from RGB frames in the sports video, such that, whenever pose is
considered reliable, the features match the output of a pretrained teacher pose
detector. Our strategy retains the best of both pose and end-to-end worlds,
exploiting the rich visual patterns in raw video frames, while learning
features that agree with the athletes' pose and motion in the target video
domain to avoid over-fitting to patterns unrelated to athletes' motion.
  VPD features improve performance on few-shot, fine-grained action
recognition, retrieval, and detection tasks in four real-world sports video
datasets, without requiring additional ground-truth pose annotations.
