  Human gait recognition is crucial in multimedia, enabling identification
through walking patterns without direct interaction, enhancing the integration
across various media forms in real-world applications like smart homes,
healthcare and non-intrusive security. LiDAR's ability to capture depth makes
it pivotal for robotic perception and holds promise for real-world gait
recognition. In this paper, based on a single LiDAR, we present the
Hierarchical Multi-representation Feature Interaction Network (HMRNet) for
robust gait recognition. Prevailing LiDAR-based gait datasets primarily derive
from controlled settings with predefined trajectory, remaining a gap with
real-world scenarios. To facilitate LiDAR-based gait recognition research, we
introduce FreeGait, a comprehensive gait dataset from large-scale,
unconstrained settings, enriched with multi-modal and varied 2D/3D data.
Notably, our approach achieves state-of-the-art performance on prior dataset
(SUSTech1K) and on FreeGait. Code and dataset will be released upon publication
of this paper.
