  Skeleton-based person re-identification (Re-ID) is an emerging open topic
providing great value for safety-critical applications. Existing methods
typically extract hand-crafted features or model skeleton dynamics from the
trajectory of body joints, while they rarely explore valuable relation
information contained in body structure or motion. To fully explore body
relations, we construct graphs to model human skeletons from different levels,
and for the first time propose a Multi-level Graph encoding approach with
Structural-Collaborative Relation learning (MG-SCR) to encode discriminative
graph features for person Re-ID. Specifically, considering that
structurally-connected body components are highly correlated in a skeleton, we
first propose a multi-head structural relation layer to learn different
relations of neighbor body-component nodes in graphs, which helps aggregate key
correlative features for effective node representations. Second, inspired by
the fact that body-component collaboration in walking usually carries
recognizable patterns, we propose a cross-level collaborative relation layer to
infer collaboration between different level components, so as to capture more
discriminative skeleton graph features. Finally, to enhance graph dynamics
encoding, we propose a novel self-supervised sparse sequential prediction task
for model pre-training, which facilitates encoding high-level graph semantics
for person Re-ID. MG-SCR outperforms state-of-the-art skeleton-based methods,
and it achieves superior performance to many multi-modal methods that utilize
extra RGB or depth features. Our codes are available at
https://github.com/Kali-Hac/MG-SCR.
