  With rapid advancements in depth sensors and deep learning, skeleton-based
person re-identification (re-ID) models have recently achieved remarkable
progress with many advantages. Most existing solutions learn single-level
skeleton features from body joints with the assumption of equal skeleton
importance, while they typically lack the ability to exploit more informative
skeleton features from various levels such as limb level with more global body
patterns. The label dependency of these methods also limits their flexibility
in learning more general skeleton representations. This paper proposes a
generic unsupervised Hierarchical skeleton Meta-Prototype Contrastive learning
(Hi-MPC) approach with Hard Skeleton Mining (HSM) for person re-ID with
unlabeled 3D skeletons. Firstly, we construct hierarchical representations of
skeletons to model coarse-to-fine body and motion features from the levels of
body joints, components, and limbs. Then a hierarchical meta-prototype
contrastive learning model is proposed to cluster and contrast the most typical
skeleton features ("prototypes") from different-level skeletons. By converting
original prototypes into meta-prototypes with multiple homogeneous
transformations, we induce the model to learn the inherent consistency of
prototypes to capture more effective skeleton features for person re-ID.
Furthermore, we devise a hard skeleton mining mechanism to adaptively infer the
informative importance of each skeleton, so as to focus on harder skeletons to
learn more discriminative skeleton representations. Extensive evaluations on
five datasets demonstrate that our approach outperforms a wide variety of
state-of-the-art skeleton-based methods. We further show the general
applicability of our method to cross-view person re-ID and RGB-based scenarios
with estimated skeletons.
