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