  Person re-identification via 3D skeletons is an emerging topic with great
potential in security-critical applications. Existing methods typically learn
body and motion features from the body-joint trajectory, whereas they lack a
systematic way to model body structure and underlying relations of body
components beyond the scale of body joints. In this paper, we for the first
time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE)
framework that comprehensively models human body, component relations, and
skeleton dynamics from unlabeled skeleton graphs of various scales to learn an
effective skeleton representation for person Re-ID. Specifically, we first
devise multi-scale skeleton graphs with coarse-to-fine human body partitions,
which enables us to model body structure and skeleton dynamics at multiple
levels. Second, to mine inherent correlations between body components in
skeletal motion, we propose a multi-scale graph relation network to learn
structural relations between adjacent body-component nodes and collaborative
relations among nodes of different scales, so as to capture more discriminative
skeleton graph features. Last, we propose a novel multi-scale skeleton
reconstruction mechanism to enable our framework to encode skeleton dynamics
and high-level semantics from unlabeled skeleton graphs, which encourages
learning a discriminative skeleton representation for person Re-ID. Extensive
experiments show that SM-SGE outperforms most state-of-the-art skeleton-based
methods. We further demonstrate its effectiveness on 3D skeleton data estimated
from large-scale RGB videos. Our codes are open at
https://github.com/Kali-Hac/SM-SGE.
