  Recent advances in skeleton-based person re-identification (re-ID) obtain
impressive performance via either hand-crafted skeleton descriptors or skeleton
representation learning with deep learning paradigms. However, they typically
require skeletal pre-modeling and label information for training, which leads
to limited applicability of these methods. In this paper, we focus on
unsupervised skeleton-based person re-ID, and present a generic Simple Masked
Contrastive learning (SimMC) framework to learn effective representations from
unlabeled 3D skeletons for person re-ID. Specifically, to fully exploit
skeleton features within each skeleton sequence, we first devise a masked
prototype contrastive learning (MPC) scheme to cluster the most typical
skeleton features (skeleton prototypes) from different subsequences randomly
masked from raw sequences, and contrast the inherent similarity between
skeleton features and different prototypes to learn discriminative skeleton
representations without using any label. Then, considering that different
subsequences within the same sequence usually enjoy strong correlations due to
the nature of motion continuity, we propose the masked intra-sequence
contrastive learning (MIC) to capture intra-sequence pattern consistency
between subsequences, so as to encourage learning more effective skeleton
representations for person re-ID. Extensive experiments validate that the
proposed SimMC outperforms most state-of-the-art skeleton-based methods. We
further show its scalability and efficiency in enhancing the performance of
existing models. Our codes are available at https://github.com/Kali-Hac/SimMC.
