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.

通过无需显式人工标注的自监督学习方法，我们提出了一种名为GaitMorph的新颖方法，它可以修改输入步态序列的变化，从而为步态识别系统提供更多的数据变异和模拟额外的步行变化。我们的方法基于高压缩模型来训练步态骨架序列，利用无标签数据构建离散且可解释的潜在空间，保留与身份相关的特征，并提出了基于最优传输理论的方法来学习离散码本上的潜在传输映射，以在不同变化之间变形步态序列。通过广泛的实验证明，我们的方法适用于为输入序列合成额外视图。

GaitMorph: 优化离散码传输的步态变换