Prior-Free Augmentation for Cloth-Changing Person Re-Identification

Cloth-changing Person Re-Identification (CCReID) aims to recognize individuals across clothing variations by learning clothing-invariant representations. However, obtaining sufficient samples of the same person in diverse outfits is often impractical. While synthesizing realistic person images provides an effective solution, existing augmentation methods require labeled data and external priors (e.g., pose skeletons, semantic maps), resulting in high costs and limited generalization. To this end, we propose a Prior-Free Augmentation method for Cloth-changing person re-identification (PFAC), which leverages text guidance to synthesize images with clothing variations while maintaining identity consistency. Our approach features: (1) a truncated diffusion model that preserves clothing-invariant structural cues from intermediate noisy images, (2) a dual-branch denoising network that decouples text-guided clothing synthesis from identity consistency via cross-modal alignment, and (3) a joint optimization strategy with identity-focused losses and image filtering to enhance realism and discriminability. Experimental results on PRCC, LTCC, and Celeb-reID datasets demonstrate that PFAC achieves state-of-the-art CCReID performance, effectively generating high-fidelity, identity-consistent images for robust augmentation without external priors.