  Person re-identification (person Re-Id) aims to retrieve the pedestrian
images of a same person that captured by disjoint and non-overlapping cameras.
Lots of researchers recently focuse on this hot issue and propose deep learning
based methods to enhance the recognition rate in a supervised or unsupervised
manner. However, two limitations that cannot be ignored: firstly, compared with
other image retrieval benchmarks, the size of existing person Re-Id datasets
are far from meeting the requirement, which cannot provide sufficient
pedestrian samples for the training of deep model; secondly, the samples in
existing datasets do not have sufficient human motions or postures coverage to
provide more priori knowledges for learning. In this paper, we introduce a
novel unsupervised pose augmentation cross-view person Re-Id scheme called
PAC-GAN to overcome these limitations. We firstly present the formal definition
of cross-view pose augmentation and then propose the framework of PAC-GAN that
is a novel conditional generative adversarial network (CGAN) based approach to
improve the performance of unsupervised corss-view person Re-Id. Specifically,
The pose generation model in PAC-GAN called CPG-Net is to generate enough
quantity of pose-rich samples from original image and skeleton samples. The
pose augmentation dataset is produced by combining the synthesized pose-rich
samples with the original samples, which is fed into the corss-view person
Re-Id model named Cross-GAN. Besides, we use weight-sharing strategy in the
CPG-Net to improve the quality of new generated samples. To the best of our
knowledge, we are the first try to enhance the unsupervised cross-view person
Re-Id by pose augmentation, and the results of extensive experiments show that
the proposed scheme can combat the state-of-the-arts.
