  Gait recognition stands as one of the most pivotal remote identification
technologies and progressively expands across research and industry
communities. However, existing gait recognition methods heavily rely on
task-specific upstream driven by supervised learning to provide explicit gait
representations like silhouette sequences, which inevitably introduce expensive
annotation costs and potential error accumulation. Escaping from this trend,
this work explores effective gait representations based on the all-purpose
knowledge produced by task-agnostic Large Vision Models (LVMs) and proposes a
simple yet efficient gait framework, termed BigGait. Specifically, the Gait
Representation Extractor (GRE) within BigGait draws upon design principles from
established gait representations, effectively transforming all-purpose
knowledge into implicit gait representations without requiring third-party
supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that
BigGait significantly outperforms the previous methods in both within-domain
and cross-domain tasks in most cases, and provides a more practical paradigm
for learning the next-generation gait representation. Finally, we delve into
prospective challenges and promising directions in LVMs-based gait recognition,
aiming to inspire future work in this emerging topic. The source code is
available at https://github.com/ShiqiYu/OpenGait.
