InfinitePerson: Innovating Synthetic Data Creation for Generalization Person Re-Identification

Recently, large-scale synthetic datasets have effectively alleviated the issue of insufficient person re-identification (Re-ID) datasets. However, synthetic datasets grapple with inherent challenges, including the subpar quality of synthetic pedestrians and single data collection. This paper presents InfinitePerson, a costless pipeline that fully utilizes the infinite generation capability of diffusion models to produce diverse UV texture images and effortlessly constructs high-quality synthetic datasets by simulating a real surveillance network. Specifically, we innovatively propose the utilization of diffusion models to generate high-quality, realistic, and diverse UV texture images to address the limitations of clothing textures. This ensures that our 3D character models have complete clothing texture information and look very similar to real-world pedestrians. Moreover, in response to the challenges in replicating synthetic data collection pipelines, we propose a sub-monitoring network data collection method, which can collect pedestrians data from different viewpoints, backgrounds, and lighting conditions through simple scene layout. Finally, a more scalable and realistic large synthetic dataset called InfinitePerson is created, containing 4,700 identities and 535,636 images. Experimental evidence demonstrates show that models trained on InfinitePerson exhibit superior generalization performance, surpassing those trained on both popular real-world and synthetic person Re-ID datasets. The InfinitePerson project is available at https://github.com/zhguoqing/InfinitePerson.