Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking
This addresses the need for fast and accurate motion annotation in applications like animation or robotics, but appears incremental as it builds on existing data-centric and contrastive learning approaches.
The paper tackles the problem of motion annotation by proposing a method that ranks motion data based on representativeness in a learned representation space, achieving superior results on the HDM05 dataset compared to state-of-the-art methods.
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.