  Most existing gait recognition methods are appearance-based, which rely on
the silhouettes extracted from the video data of human walking activities. The
less-investigated skeleton-based gait recognition methods directly learn the
gait dynamics from 2D/3D human skeleton sequences, which are theoretically more
robust solutions in the presence of appearance changes caused by clothes,
hairstyles, and carrying objects. However, the performance of skeleton-based
solutions is still largely behind the appearance-based ones. This paper aims to
close such performance gap by proposing a novel network model, GaitMixer, to
learn more discriminative gait representation from skeleton sequence data. In
particular, GaitMixer follows a heterogeneous multi-axial mixer architecture,
which exploits the spatial self-attention mixer followed by the temporal
large-kernel convolution mixer to learn rich multi-frequency signals in the
gait feature maps. Experiments on the widely used gait database, CASIA-B,
demonstrate that GaitMixer outperforms the previous SOTA skeleton-based methods
by a large margin while achieving a competitive performance compared with the
representative appearance-based solutions. Code will be available at
https://github.com/exitudio/gaitmixer
