  Gait recognition, a rapidly advancing vision technology for person
identification from a distance, has made significant strides in indoor
settings. However, evidence suggests that existing methods often yield
unsatisfactory results when applied to newly released real-world gait datasets.
Furthermore, conclusions drawn from indoor gait datasets may not easily
generalize to outdoor ones. Therefore, the primary goal of this work is to
present a comprehensive benchmark study aimed at improving practicality rather
than solely focusing on enhancing performance. To this end, we first develop
OpenGait, a flexible and efficient gait recognition platform. Using OpenGait as
a foundation, we conduct in-depth ablation experiments to revisit recent
developments in gait recognition. Surprisingly, we detect some imperfect parts
of certain prior methods thereby resulting in several critical yet undiscovered
insights. Inspired by these findings, we develop three structurally simple yet
empirically powerful and practically robust baseline models, i.e., DeepGaitV2,
SkeletonGait, and SkeletonGait++, respectively representing the
appearance-based, model-based, and multi-modal methodology for gait pattern
description. Beyond achieving SoTA performances, more importantly, our careful
exploration sheds new light on the modeling experience of deep gait models, the
representational capacity of typical gait modalities, and so on. We hope this
work can inspire further research and application of gait recognition towards
better practicality. The code is available at
https://github.com/ShiqiYu/OpenGait.
