Gait recognition plays an important role in video surveillance and security by identifying humans based on their unique walking patterns. The existing gait recognition methods have achieved competitive accuracy with shape and motion patterns under limited-covariate conditions. However, when extreme appearance changes distort discriminative features, gait recognition yields unsatisfactory results under cross-covariate conditions. In this work, we first indicate that the integral pose in each silhouette maintains an appearance-unrelated discriminative identity. However, the monotonous appearance variables in a gait database cause gait models to have difficulty extracting integral poses. Therefore, we propose an Appearance-transferable Disentangling and Generative Network (GaitApp) to generate gait silhouettes with rich appearances and invariant poses. Specifically, GaitApp leverages multi-branch cooperation to disentangle pose features and appearance features, and transfers the appearance information from one subject to another. By simulating a person constantly changing appearances under limited-covariate conditions, downstream models enable to extract discriminative integral pose features. Extensive experiments demonstrate that our method allows representative gait models to stand at a new altitude, further promoting the exploration to cross-covariate gait recognition. All the code is available at https://github.com/Hpjhpjhs/GaitApp.git

中文翻译：

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通过外观迁移进行整体姿势学习以进行步态识别

步态识别通过根据独特的行走模式来识别人类，在视频监控和安全中发挥着重要作用。现有的步态识别方法在有限协变量条件下已经在形状和运动模式方面取得了有竞争力的准确性。然而，当极端的外观变化扭曲了判别特征时，步态识别在跨协变量条件下会产生不令人满意的结果。在这项工作中，我们首先表明每个轮廓中的整体姿势保持与外观无关的判别身份。然而，步态数据库中单调的外观变量导致步态模型难以提取完整的姿势。因此，我们提出了一种外观可转移的解缠和生成网络（GaitApp）来生成具有丰富外观和不变姿势的步态轮廓。具体来说，GaitApp 利用多分支协作来解开姿势特征和外观特征，并将外观信息从一个主体传输到另一个主体。通过模拟一个人在有限协变量条件下不断变化的外观，下游模型能够提取有区别的整体姿势特征。大量实验表明，我们的方法允许代表性步态模型站在新的高度，进一步推动了跨协变量步态识别的探索。所有代码均可在https://github.com/Hpjhpjhs/GaitApp.git