  Person re-identification (re-ID) via 3D skeletons is an important emerging
topic with many merits. Existing solutions rarely explore valuable
body-component relations in skeletal structure or motion, and they typically
lack the ability to learn general representations with unlabeled skeleton data
for person re-ID. This paper proposes a generic unsupervised Skeleton Prototype
Contrastive learning paradigm with Multi-level Graph Relation learning
(SPC-MGR) to learn effective representations from unlabeled skeletons to
perform person re-ID. Specifically, we first construct unified multi-level
skeleton graphs to fully model body structure within skeletons. Then we propose
a multi-head structural relation layer to comprehensively capture relations of
physically-connected body-component nodes in graphs. A full-level collaborative
relation layer is exploited to infer collaboration between motion-related body
parts at various levels, so as to capture rich body features and recognizable
walking patterns. Lastly, we propose a skeleton prototype contrastive learning
scheme that clusters feature-correlative instances of unlabeled graph
representations and contrasts their inherent similarity with representative
skeleton features ("skeleton prototypes") to learn discriminative skeleton
representations for person re-ID. Empirical evaluations show that SPC-MGR
significantly outperforms several state-of-the-art skeleton-based methods, and
it also achieves highly competitive person re-ID performance for more general
scenarios.
