  Gait recognition has achieved promising advances in controlled settings, yet
it significantly struggles in unconstrained environments due to challenges such
as view changes, occlusions, and varying walking speeds. Additionally, efforts
to fuse multiple modalities often face limited improvements because of
cross-modality incompatibility, particularly in outdoor scenarios. To address
these issues, we present a multi-modal Hierarchy in Hierarchy network (HiH)
that integrates silhouette and pose sequences for robust gait recognition. HiH
features a main branch that utilizes Hierarchical Gait Decomposer (HGD) modules
for depth-wise and intra-module hierarchical examination of general gait
patterns from silhouette data. This approach captures motion hierarchies from
overall body dynamics to detailed limb movements, facilitating the
representation of gait attributes across multiple spatial resolutions.
Complementing this, an auxiliary branch, based on 2D joint sequences, enriches
the spatial and temporal aspects of gait analysis. It employs a Deformable
Spatial Enhancement (DSE) module for pose-guided spatial attention and a
Deformable Temporal Alignment (DTA) module for aligning motion dynamics through
learned temporal offsets. Extensive evaluations across diverse indoor and
outdoor datasets demonstrate HiH's state-of-the-art performance, affirming a
well-balanced trade-off between accuracy and efficiency.
