Uli-Ri: A Benchmark for Person Re-Identification With Quantitative Annotations

Person re-identification (re-ID) has wide applications in surveillance and security. It is also challenging due to viewpoint, occlusion and illumination variations across different cameras. One solution to unsupervised person re-ID problems is synthetic data augmentation. Generative neural networks have been used to translate images from the source domain into the target domain. In this paper, we introduce a new virtual-human image dataset that can be used as the source domain for person re-ID. This new dataset has images labeled by person identity, background, viewpoint and illumination intensity. We also explore GAN-based and Diffusion-based generative methods for unpaired image-to-image translation and provide qualitative and quantitative evaluation for the synthetic results.