Attribute Conditional Diffusion-Augmented Person Re-Identification

Abstract

Due to privacy and cost issues, the lack of large-scale labeled datasets limits the advancement of person re-identification. Existing methods use generative adversarial networks or game engine rendering for data augmentation to improve re-identification performance. However, these approaches struggle to maintain realistic images. This paper introduces a novel approach called Identity Diffuser, which uses diffusion models to generate synthetic data for the same identity with different poses. Our proposed framework incorporates identity-specific embeddings and target poses into the diffusion process, enabling the generation of realistic and diverse images that consistently preserve identity features. Guided by pretrained re-identification net and target pose heatmap, the framework learns transformation trajectories through forward and backward denoising steps in the diffusion models. This approach effectively maintains key pedestrian attributes across various poses. Experimental results on the Market1501 and DukeMTMC datasets demonstrate a notable improvement in performance, with a 1.73%/0.80% mAp increase in Market1501/DukeMTMC datasets compared with current state-of-the-art method. When less real data is included, the increment can be 5.1%/1.5%, separately.