GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes
Adrian Cosma Emilian Radoi
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.