  Person re-identification (Re-ID) via gait features within 3D skeleton
sequences is a newly-emerging topic with several advantages. Existing solutions
either rely on hand-crafted descriptors or supervised gait representation
learning. This paper proposes a self-supervised gait encoding approach that can
leverage unlabeled skeleton data to learn gait representations for person
Re-ID. Specifically, we first create self-supervision by learning to
reconstruct unlabeled skeleton sequences reversely, which involves richer
high-level semantics to obtain better gait representations. Other pretext tasks
are also explored to further improve self-supervised learning. Second, inspired
by the fact that motion's continuity endows adjacent skeletons in one skeleton
sequence and temporally consecutive skeleton sequences with higher correlations
(referred as locality in 3D skeleton data), we propose a locality-aware
attention mechanism and a locality-aware contrastive learning scheme, which aim
to preserve locality-awareness on intra-sequence level and inter-sequence level
respectively during self-supervised learning. Last, with context vectors
learned by our locality-aware attention mechanism and contrastive learning
scheme, a novel feature named Constrastive Attention-based Gait Encodings
(CAGEs) is designed to represent gait effectively. Empirical evaluations show
that our approach significantly outperforms skeleton-based counterparts by
15-40% Rank-1 accuracy, and it even achieves superior performance to numerous
multi-modal methods with extra RGB or depth information. Our codes are
available at https://github.com/Kali-Hac/Locality-Awareness-SGE.
