  Gait recognition is a rapidly advancing vision technique for person
identification from a distance. Prior studies predominantly employed relatively
shallow networks to extract subtle gait features, achieving impressive
successes in constrained settings. Nevertheless, experiments revealed that
existing methods mostly produce unsatisfactory results when applied to newly
released real-world gait datasets. This paper presents a unified perspective to
explore how to construct deep models for state-of-the-art outdoor gait
recognition, including the classical CNN-based and emerging Transformer-based
architectures. Specifically, we challenge the stereotype of shallow gait models
and demonstrate the superiority of explicit temporal modeling and deep
transformer structure for discriminative gait representation learning.
Consequently, the proposed CNN-based DeepGaitV2 series and Transformer-based
SwinGait series exhibit significant performance improvements on Gait3D and
GREW. As for the constrained gait datasets, the DeepGaitV2 series also reaches
a new state-of-the-art in most cases, convincingly showing its practicality and
generality. The source code is available at
https://github.com/ShiqiYu/OpenGait.
