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 Generalized Pose-based Gait recognition (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. The code will be released.

翻译：近期基于姿态的步态识别研究表明，利用这种简单信息即可取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却明显逊于基于轮廓的方法，这一问题虽未引起足够重视，却阻碍了此类方法在真实场景中的应用。为提升基于姿态的方法跨数据集的泛化能力，我们提出了一种基于姿态的通用步态识别（GPGait）框架。首先，提出面向人体变换（HOT）与一系列面向人体描述符（HOD），以获取具有判别性多特征的统一姿态表征。其次，鉴于经HOT与HOD处理后统一表征存在细微差异，网络需提取关键点间的局部-全局关系。为此，提出一种部位感知图卷积网络（PAGCN），实现高效的图划分与局部-全局空间特征提取。在CASIA-B、OUMVLP-Pose、Gait3D与GREW四个公开步态识别数据集上的实验表明，与现有基于骨架的方法相比，我们的模型展现出更优且更稳定的跨域能力，取得了与基于轮廓的方法相当的识别结果。相关代码将开源。