Pose-Normalized Image Generation for Person Re-identification

Xuelin Qian, Yanwei Fu, Tao Xiang, Wenxuan Wang, Jie Qiu, Yang Wu, Yu-Gang Jiang, Xiangyang Xue

Abstract: Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for synthesizing realistic person images conditional on the pose. The model is based on a generative adversarial network (GAN) designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN). With the synthesized images, we can learn a new type of deep re-id features free of the influence of pose variations. We show that these features are complementary to features learned with the original images. Importantly, a more realistic unsupervised learning setting is considered in this work, and our model is shown to have the potential to be generalizable to a new re-id dataset without any fine-tuning. The codes will be released at https://github.com/naiq/PN_GAN.