  The choice of the representations is essential for deep gait recognition
methods. The binary silhouettes and skeletal coordinates are two dominant
representations in recent literature, achieving remarkable advances in many
scenarios. However, inherent challenges remain, in which silhouettes are not
always guaranteed in unconstrained scenes, and structural cues have not been
fully utilized from skeletons. In this paper, we introduce a novel skeletal
gait representation named skeleton map, together with SkeletonGait, a
skeleton-based method to exploit structural information from human skeleton
maps. Specifically, the skeleton map represents the coordinates of human joints
as a heatmap with Gaussian approximation, exhibiting a silhouette-like image
devoid of exact body structure. Beyond achieving state-of-the-art performances
over five popular gait datasets, more importantly, SkeletonGait uncovers novel
insights about how important structural features are in describing gait and
when they play a role. Furthermore, we propose a multi-branch architecture,
named SkeletonGait++, to make use of complementary features from both skeletons
and silhouettes. Experiments indicate that SkeletonGait++ outperforms existing
state-of-the-art methods by a significant margin in various scenarios. For
instance, it achieves an impressive rank-1 accuracy of over 85% on the
challenging GREW dataset. All the source code is available at
https://github.com/ShiqiYu/OpenGait.
