RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations
between RGB and IR images. Considering no correspondence labels between every pair of RGB and IR
images, most methods try to alleviate the variations with set-level alignment by reducing marginal
distribution divergence between the entire RGB and IR sets. However, this set-level alignment strategy
may lead to misalignment of some instances, which limit the performance for RGB–IR Re-ID. Different
from existing methods, in this paper, we propose to generate cross-modality paired-images and
perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys
several merits. First, our method can perform set-level alignment by disentangling modality-specific
and modality-invariant features. Compared with conventional methods, ours can explicitly remove
the modality-specific features and the modality variation can be better reduced. Second, given crossmodality
unpaired-images of a person, our method can generate cross-modality paired images from
exchanged features. With them, we can directly perform instance-level alignment by minimizing
distances of every pair of images. Third, our method learns a latent manifold space. In the space,
we can random sample and generate lots of images of unseen classes. Training with those images,
the learned identity feature space is more smooth can generalize better when test. Finally, extensive
experimental results on two standard benchmarks demonstrate that the proposed model favorably
against state-of-the-art methods.