  Person re-identification (re-ID) via 3D skeleton data is an emerging topic
with prominent advantages. Existing methods usually design skeleton descriptors
with raw body joints or perform skeleton sequence representation learning.
However, they typically cannot concurrently model different body-component
relations, and rarely explore useful semantics from fine-grained
representations of body joints. In this paper, we propose a generic
Transformer-based Skeleton Graph prototype contrastive learning (TranSG)
approach with structure-trajectory prompted reconstruction to fully capture
skeletal relations and valuable spatial-temporal semantics from skeleton graphs
for person re-ID. Specifically, we first devise the Skeleton Graph Transformer
(SGT) to simultaneously learn body and motion relations within skeleton graphs,
so as to aggregate key correlative node features into graph representations.
Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the
most typical graph features (graph prototypes) of each identity, and contrast
the inherent similarity between graph representations and different prototypes
from both skeleton and sequence levels to learn discriminative graph
representations. Last, a graph Structure-Trajectory Prompted Reconstruction
(STPR) mechanism is proposed to exploit the spatial and temporal contexts of
graph nodes to prompt skeleton graph reconstruction, which facilitates
capturing more valuable patterns and graph semantics for person re-ID.
Empirical evaluations demonstrate that TranSG significantly outperforms
existing state-of-the-art methods. We further show its generality under
different graph modeling, RGB-estimated skeletons, and unsupervised scenarios.
