  In this paper, we follow a data-centric philosophy and propose a novel motion
annotation method based on the inherent representativeness of motion data in a
given dataset. Specifically, we propose a Representation-based
Representativeness Ranking R3 method that ranks all motion data in a given
dataset according to their representativeness in a learned motion
representation space. We further propose a novel dual-level motion constrastive
learning method to learn the motion representation space in a more informative
way. Thanks to its high efficiency, our method is particularly responsive to
frequent requirements change and enables agile development of motion annotation
models. Experimental results on the HDM05 dataset against state-of-the-art
methods demonstrate the superiority of our method.
