Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce spatiotemporal distortions that impede structure-sensitive downstream applications. We propose GaitProtector, an impersonation-driven gait de-identification framework that formulates privacy protection as a unified objective with two tightly coupled components: (i) obfuscation, which repels the protected gait from the source identity, and (ii) impersonation, which attracts it toward a selected target identity. The target identity serves as a semantic anchor that biases optimization toward structurally plausible gait patterns under the pretrained diffusion prior, helping preserve dominant body shape and motion dynamics. We instantiate this idea through a training-free diffusion latent optimization pipeline. Instead of retraining a generator for each dataset, we invert each input silhouette sequence into the latent trajectory of a pretrained 3D video diffusion model and iteratively optimize latent codes with a differentiable adversarial objective to synthesize protected gaits. Experiments on the CASIA-B dataset show that GaitProtector achieves a 56.7% impersonation success rate under black-box gait recognition and reduces Rank-1 identification accuracy from 89.6% to 15.0%, while maintaining favorable visual and temporal quality. We further evaluate downstream utility on the Scoliosis1K dataset, where diagnostic accuracy decreases only from 91.4% to 74.2%. To the best of our knowledge, this work is the first to leverage pretrained 3D diffusion priors in a training-free manner for silhouette-based gait de-identification.

翻译：传统步态脱识别方法常面临固有权衡：要么身份抑制不充分，要么引入时空失真，阻碍对结构敏感的下游应用。我们提出GaitProtector——一种模仿驱动的步态脱识别框架，将隐私保护形式化为具有两个紧密耦合组件的统一目标：(i)混淆，使受保护步态远离源身份；(ii)模仿，使其趋近所选目标身份。目标身份作为语义锚点，在预训练扩散先验下将优化偏向结构合理的步态模式，有助于保留主导体形与运动动力学。我们通过一种无需训练的扩散潜在优化流水线实现该思想。无需为每个数据集重新训练生成器，我们将每个输入轮廓序列反转为预训练3D视频扩散模型的潜在轨迹，并利用可微对抗目标迭代优化潜在编码以合成受保护步态。在CASIA-B数据集上的实验表明，GaitProtector在黑盒步态识别下实现56.7%的模仿成功率，并将Rank-1识别准确率从89.6%降至15.0%，同时保持良好视觉与时间质量。我们进一步在Scoliosis1K数据集上评估下游实用性，诊断准确率仅从91.4%降至74.2%。据我们所知，本工作是首次以无需训练方式利用预训练3D扩散先验实现基于轮廓的步态脱识别。