Abstract
Gait recognition is a valuable biometric task that enables the identification of individuals from a distance based on their walking patterns. However, it remains limited by the lack of large-scale labeled datasets and the difficulty of collecting diverse gait samples for each individual while preserving privacy. To address these challenges, we propose GaitCrafter, a diffusion-based framework for synthesizing realistic gait sequences in the silhouette domain. Unlike prior works that rely on simulated environments or alternative generative models, GaitCrafter trains a video diffusion model from scratch, exclusively on gait silhouette data. Our approach enables the generation of temporally consistent and identity-preserving gait sequences. Moreover, the generation process is controllable-allowing conditioning on various covariates such as clothing, carried objects, and view angle. We show that incorporating synthetic samples generated by GaitCrafter into the gait recognition pipeline leads to improved performance, especially under challenging conditions. Additionally, we introduce a mechanism to generate novel identities-synthetic individuals not present in the original dataset-by interpolating identity embeddings. These novel identities exhibit unique, consistent gait patterns and are useful for training models while maintaining privacy of real subjects. Overall, our work takes an important step toward leveraging diffusion models for high-quality, controllable, and privacy-aware gait data generation.
Abstract (translated)
步态识别是一种有价值的生物特征任务，通过个体的行走模式从远处识别个人。然而，这一领域受限于大规模标注数据集的缺乏以及在保护隐私的同时收集每位个体多样化步态样本的难度。为了应对这些挑战，我们提出了GaitCrafter，这是一种基于扩散模型的框架，用于在轮廓域中合成逼真的步态序列。与之前依赖于模拟环境或替代生成模型的方法不同，GaitCrafter从头开始仅使用步态轮廓数据训练视频扩散模型。 我们的方法能够生成时间上一致且能保持身份特征的步态序列。此外，生成过程是可控制的——可以依据各种协变量（如穿着的衣服、携带的物体和视角角度）进行条件设置。我们证明了将GaitCrafter生成的合成样本加入到步态识别流程中能够提升性能，特别是在面对挑战性情况时。 此外，我们还引入了一种机制来生成新身份——在原始数据集中不存在的合成个体，通过插值身份嵌入实现这一目标。这些新的身份具有独特的、一致的步态模式，并且对于在保护实际参与者的隐私的同时训练模型非常有用。 总的来说，我们的工作为利用扩散模型进行高质量、可控和注重隐私的步态数据生成迈出了重要一步。
URL
https://arxiv.org/abs/2508.13300
