  Recent self-supervised contrastive learning provides an effective approach
for unsupervised person re-identification (ReID) by learning invariance from
different views (transformed versions) of an input. In this paper, we
incorporate a Generative Adversarial Network (GAN) and a contrastive learning
module into one joint training framework. While the GAN provides online data
augmentation for contrastive learning, the contrastive module learns
view-invariant features for generation. In this context, we propose a
mesh-based view generator. Specifically, mesh projections serve as references
towards generating novel views of a person. In addition, we propose a
view-invariant loss to facilitate contrastive learning between original and
generated views. Deviating from previous GAN-based unsupervised ReID methods
involving domain adaptation, we do not rely on a labeled source dataset, which
makes our method more flexible. Extensive experimental results show that our
method significantly outperforms state-of-the-art methods under both, fully
unsupervised and unsupervised domain adaptive settings on several large scale
ReID datsets.
