Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.

翻译：近期基于姿态的步态识别研究已表明，利用此类简单信息能够获得与基于轮廓的方法相媲美的结果。然而，基于姿态的方法在不同数据集上的泛化能力明显弱于基于轮廓的方法——这一问题虽鲜受关注，却阻碍了此类方法在现实场景中的应用。为提升基于姿态的方法跨数据集的泛化能力，我们提出一种基于姿态的通用步态识别（GPGait）框架。首先，提出面向人体的变换（HOT）与一系列面向人体的描述符（HOD），以获取具有判别性多特征的一致化姿态表征。其次，鉴于经过HOT与HOD处理后的一致化表征存在微小差异，网络需提取关键点间的局部-全局关系。为此，我们提出部分感知图卷积网络（PAGCN），以实现高效的图分区与局部-全局空间特征提取。在四个公开步态识别数据集（CASIA-B、OUMVLP-Pose、Gait3D与GREW）上的实验表明，与现有基于骨架的方法相比，本模型展现出更优且更稳定的跨域能力，并取得了与基于轮廓的方法相当的识别结果。代码开源地址：https://github.com/BNU-IVC/FastPoseGait。