Gait, the manner of walking, has been proven to be a reliable biometric with uses in surveillance, marketing and security. A promising new direction for the field is training gait recognition systems without explicit human annotations, through self-supervised learning approaches. Such methods are heavily reliant on strong augmentations for the same walking sequence to induce more data variability and to simulate additional walking variations. Current data augmentation schemes are heuristic and cannot provide the necessary data variation as they are only able to provide simple temporal and spatial distortions. In this work, we propose GaitMorph, a novel method to modify the walking variation for an input gait sequence. Our method entails the training of a high-compression model for gait skeleton sequences that leverages unlabelled data to construct a discrete and interpretable latent space, which preserves identity-related features. Furthermore, we propose a method based on optimal transport theory to learn latent transport maps on the discrete codebook that morph gait sequences between variations. We perform extensive experiments and show that our method is suitable to synthesize additional views for an input sequence.

翻译：[translated abstract in Chinese] 步态作为行走方式，已被证明是一种可靠的生物特征，在监控、市场营销和安全领域具有应用价值。该领域一个颇具前景的新方向是通过自监督学习方法，在无需人工标注的情况下训练步态识别系统。此类方法高度依赖于对同一行走序列进行强数据增强，以增加数据变异性并模拟额外的行走变化。现有数据增强方案多为启发式方法，仅能提供简单的时间与空间扭曲，无法提供必要的数据变异。本文提出GaitMorph——一种针对输入步态序列修改行走变化的新方法。该方法首先训练一个步态骨架序列的高压缩模型，利用无标注数据构建离散且可解释的潜空间，从而保留与身份相关的特征。此外，我们基于最优传输理论提出了一种方法，在离散码本上学习潜传输映射，以实现不同变化下步态序列的形态变换。通过大量实验证明，该方法能够有效为输入序列合成额外视角的数据。