  Gait benchmarks empower the research community to train and evaluate
high-performance gait recognition systems. Even though growing efforts have
been devoted to cross-view recognition, academia is restricted by current
existing databases captured in the controlled environment. In this paper, we
contribute a new benchmark and strong baseline for Gait REcognition in the Wild
(GREW). The GREW dataset is constructed from natural videos, which contain
hundreds of cameras and thousands of hours of streams in open systems. With
tremendous manual annotations, the GREW consists of 26K identities and 128K
sequences with rich attributes for unconstrained gait recognition. Moreover, we
add a distractor set of over 233K sequences, making it more suitable for
real-world applications. Compared with prevailing predefined cross-view
datasets, the GREW has diverse and practical view variations, as well as more
naturally challenging factors. To the best of our knowledge, this is the first
large-scale dataset for gait recognition in the wild. Equipped with this
benchmark, we dissect the unconstrained gait recognition problem, where
representative appearance-based and model-based methods are explored. The
proposed GREW benchmark proves to be essential for both training and evaluating
gait recognizers in unconstrained scenarios. In addition, we propose the Single
Path One-Shot neural architecture search with uniform sampling for Gait
recognition, named SPOSGait, which is the first NAS-based gait recognition
model. In experiments, SPOSGait achieves state-of-the-art performance on the
CASIA-B, OU-MVLP, Gait3D, and GREW benchmarks, outperforming existing
approaches by a large margin. The code will be released at
https://github.com/XiandaGuo/SPOSGait.
