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.

翻译：本文遵循数据驱动的理念，提出了一种新颖的运动注释方法，该方法基于给定数据集中运动数据的内在代表性。具体而言，我们提出了基于表征的代表性排序（R3）方法，该方法根据运动数据在所学运动表征空间中的代表性，对给定数据集中的所有运动数据进行排序。我们进一步提出了一种新颖的双层级运动对比学习方法，以更具信息量的方式学习运动表征空间。得益于其高效性，我们的方法能够快速响应频繁的需求变化，实现运动注释模型的敏捷开发。在HDM05数据集上，与现有最先进方法的实验结果表明了本方法的优越性。