Language-Guided and Motion-Aware Gait Representation for Generalizable Recognition

Gait recognition is emerging as a promising technology and an innovative field within computer vision. However, existing methods typically rely on complex architectures to directly extract features from images and apply pooling operations to obtain sequence-level representations. Such designs often lead to overfitting on static noise (e.g., clothing), while failing to effectively capture dynamic motion regions.To address the above challenges, we present a Language guided and Motion-aware gait recognition framework, named LMGait.In particular, we utilize designed gait-related language cues to capture key motion features in gait sequences.