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.

翻译：作为一种生物特征，行人的步态信息因其可在远距离无需目标配合的情况下获取，已引起工业界和学术界的广泛关注。近期文献中，该研究方向在带来令人振奋机遇的同时，也引发了严峻挑战：积极方面，用于嫌疑人员检索和安全检查等安防应用的步态识别技术日益成熟；消极方面，步态信息滥用可能导致隐私问题——即使在佩戴口罩和更换衣物的场景下，不法分子仍可利用步态特征追踪感兴趣的目标。为应对这一双刃剑问题，我们提出名为GaitEditor的步态属性编辑框架。该框架能在保持视觉真实性的前提下，对真实步态序列进行不同程度的属性编辑，分别用于步态数据增广和去身份化，从而根据用户意图自适应增强或降低步态识别性能。实验方面，我们在三个广泛使用的步态基准上，同时采用步态识别和匿名化协议进行综合评估。大量结果表明，GaitEditor的灵活运用能有效提升步态识别性能，并生成具有去身份化效果的真实可视化结果以保护人类隐私。据我们所知，GaitEditor是首个能够同时编辑多种步态属性，并兼顾步态识别与步态匿名化的框架。GaitEditor源代码将发布于https://github.com/ShiqiYu/OpenGait。