GaitWM: Generative World Models for Sparse-View Gait Recognition

Gait recognition has achieved remarkable progress with the advancement of deep learning, showing promising performance in both controlled environments and unconstrained real-world scenarios. Existing cross-view gait recognition methods typically rely on densely collected multi-view gait sequences to learn view-consistent representations, assuming sufficient observations across predefined viewpoints. However, such assumptions rarely hold in practical applications, where gait data are often collected from sparse and unpredictable viewpoints due to the high cost and difficulty of multi-view acquisition and annotation. This limitation significantly degrades recognition performance when encountering unseen viewpoints. To address this challenge, we propose $\text{GaitWM}$, utilizing a $\text{generative world model}$ for label-efficient $\text{sparse-view gait recognition}$. Unlike conventional methods that learn view-invariant representations solely from existing observations, GaitWM learns the underlying gait dynamics and view transformation knowledge from generative video world, enabling the generation of plausible gait sequences from unobserved viewpoints. By synthesizing complementary views conditioned on sparse observations, GaitWM effectively expands viewpoint coverage and provides richer motion cues for recognition without requiring densely collected multi-view gait datasets. Extensive experiments on CASIA-B and SUSTech1K demonstrate that GaitWM consistently outperforms existing methods under sparse-view settings, achieving robust generalization to unseen viewpoints. The results validate the effectiveness of generative video models as a powerful paradigm for reducing reliance on exhaustive multi-view gait data collection and advancing practical gait recognition systems. $\text{The code will be released publicly upon publication.}$