  Style variation has been a major challenge for person re-identification,
which aims to match the same pedestrians across different cameras. Existing
works attempted to address this problem with camera-invariant descriptor
subspace learning. However, there will be more image artifacts when the
difference between the images taken by different cameras is larger. To solve
this problem, we propose a UnityStyle adaption method, which can smooth the
style disparities within the same camera and across different cameras.
Specifically, we firstly create UnityGAN to learn the style changes between
cameras, producing shape-stable style-unity images for each camera, which is
called UnityStyle images. Meanwhile, we use UnityStyle images to eliminate
style differences between different images, which makes a better match between
query and gallery. Then, we apply the proposed method to Re-ID models,
expecting to obtain more style-robust depth features for querying. We conduct
extensive experiments on widely used benchmark datasets to evaluate the
performance of the proposed framework, the results of which confirm the
superiority of the proposed model.
