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 do 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 will be available at https://github.com/ShiqiYu/OpenGait.

翻译：表示形式的选择对于深度步态识别方法至关重要。二值轮廓图和骨骼坐标是近期文献中两种主流表示方法，已在许多场景中取得显著进展。然而，仍存在固有挑战：在非约束场景中轮廓图不一定可靠，且骨骼的结构线索未被充分利用。本文提出一种名为Skeleton Map的新型骨骼步态表示形式，以及基于骨骼的SkeletonGait方法，用于从人体骨骼图中挖掘结构信息。具体而言，骨骼图通过高斯近似将人体关节坐标表示为热力图，呈现出缺乏精确身体结构的类轮廓图像形态。该方法不仅在五个主流步态数据集上达到最先进性能，更重要的是，SkeletonGait揭示了关于结构特征对步态描述的重要性及其作用时机的新见解。此外，本文提出名为SkeletonGait++的多分支架构，以融合来自骨骼和轮廓图的互补特征。实验表明，SkeletonGait++在各种场景下均大幅超越现有最先进方法。例如，在具有挑战性的GREW数据集上，其Rank-1准确率超过85%。所有源代码将公开于https://github.com/ShiqiYu/OpenGait。