Abstract
To capture individual gait patterns, excluding identity-irrelevant cues in walking videos, such as clothing texture and color, remains a persistent challenge for vision-based gait recognition. Traditional silhouette- and pose-based methods, though theoretically effective at removing such distractions, often fall short of high accuracy due to their sparse and less informative inputs. Emerging end-to-end methods address this by directly denoising RGB videos using human priors. Building on this trend, we propose DenoisingGait, a novel gait denoising method. Inspired by the philosophy that "what I cannot create, I do not understand", we turn to generative diffusion models, uncovering how they partially filter out irrelevant factors for gait understanding. Additionally, we introduce a geometry-driven Feature Matching module, which, combined with background removal via human silhouettes, condenses the multi-channel diffusion features at each foreground pixel into a two-channel direction vector. Specifically, the proposed within- and cross-frame matching respectively capture the local vectorized structures of gait appearance and motion, producing a novel flow-like gait representation termed Gait Feature Field, which further reduces residual noise in diffusion features. Experiments on the CCPG, CASIA-B*, and SUSTech1K datasets demonstrate that DenoisingGait achieves a new SoTA performance in most cases for both within- and cross-domain evaluations. Code is available at this https URL.
Abstract (translated)
要捕捉个体步态模式，排除走路视频中的身份无关线索（如衣物纹理和颜色），对于基于视觉的步态识别来说一直是一项挑战。传统的轮廓和姿势方法虽然理论上能有效去除这些干扰因素，但由于其稀疏且信息量较少的输入，在实际应用中往往难以达到高精度。新兴的端到端方法通过直接利用人体先验知识对RGB视频进行去噪来解决这一问题。在此基础上，我们提出了DenoisingGait，一种新型步态去噪方法。 受“我无法创造的东西我不理解”的哲学思想启发，我们将注意力转向生成扩散模型，并发现这些模型部分地过滤出了与步态理解无关的因素。此外，我们还引入了一个几何驱动的特征匹配模块，在通过人体轮廓移除背景后，将多通道扩散特征在每个前景像素处压缩成两个方向向量。 具体来说，提出的帧内和跨帧匹配分别捕捉了步态外观和运动的局部矢量化结构，生成了一种新的流式步态表示，称为步态特征场（Gait Feature Field），进一步减少了扩散特征中的残余噪声。在CCPG、CASIA-B* 和 SUSTech1K 数据集上的实验表明，在域内和跨域评估中，DenoisingGait 在大多数情况下实现了新的最佳性能。代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2505.18582
