  As a kind of biometrics, the gait information of pedestrians has attracted
widespread attention from both industry and academia since it can be acquired
from long distances without the cooperation of targets. In recent literature,
this line of research has brought exciting chances along with alarming
challenges: On the positive side, gait recognition used for security
applications such as suspect retrieval and safety checks is becoming more and
more promising. On the negative side, the misuse of gait information may lead
to privacy concerns, as lawbreakers can track subjects of interest using gait
characteristics even under face-masked and clothes-changed scenarios. To handle
this double-edged sword, we propose a gait attribute editing framework termed
GaitEditor. It can perform various degrees of attribute edits on real gait
sequences while maintaining the visual authenticity, respectively used for gait
data augmentation and de-identification, thereby adaptively enhancing or
degrading gait recognition performance according to users' intentions.
Experimentally, we conduct a comprehensive evaluation under both gait
recognition and anonymization protocols on three widely used gait benchmarks.
Numerous results illustrate that the adaptable utilization of GaitEditor
efficiently improves gait recognition performance and generates vivid
visualizations with de-identification to protect human privacy. To the best of
our knowledge, GaitEditor is the first framework capable of editing multiple
gait attributes while simultaneously benefiting gait recognition and gait
anonymization. The source code of GaitEditor will be available at
https://github.com/ShiqiYu/OpenGait.
