PerDiff: identity-preserving diffusion reconstruction for person re-identification

Abstract Person re-identification (ReID) poses significant challenges in practical surveillance scenarios, where identity-discriminative information is often degraded by occlusion, low resolution, motion blur, and varying illumination. While diffusion-based models have recently shown strong potential in image generation, their use in ReID has been largely limited to data augmentation, leaving their potential for directly restoring identity-relevant information from degraded probe images largely unexplored. In this paper, we introduce PerDiff , a novel diffusion-driven framework for identity-preserving reconstruction in person ReID. Unlike conventional generative approaches, PerDiff reformulates diffusion as a task-driven, identity-aware inverse process, where corrupted person images are progressively denoised to recover identity-discriminative visual cues prior to feature extraction. By tightly coupling diffusion-based reconstruction with identity recognition, PerDiff produces identity-consistent representations that are explicitly optimized for ReID matching rather than visual realism alone. Extensive evaluations on four benchmark datasets, including Market-1501, CUHK03, DukeMTMC-reID, and IUST_PersonReID, demonstrate that PerDiff consistently improves ReID performance in both mean Average Precision and Rank-1 scores, particularly under severe degradation conditions. These results establish diffusion-based identity reconstruction as a powerful new paradigm for robust person ReID beyond traditional data augmentation strategies.