  Clothing changes and lack of data labels are both crucial challenges in
person ReID. For the former challenge, people may occur multiple times at
different locations wearing different clothing. However, most of the current
person ReID research works focus on the benchmarks in which a person's clothing
is kept the same all the time. For the last challenge, some researchers try to
make model learn information from a labeled dataset as a source to an unlabeled
dataset. Whereas purely unsupervised training is less used. In this paper, we
aim to solve both problems at the same time. We design a novel unsupervised
model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person
ReID problem. We developer a purely unsupervised clothing change person ReID
pipeline with person sync augmentation operation and same person feature
restriction. The person sync augmentation is to supply additional same person
resources. These same person's resources can be used as part supervised input
by same person feature restriction. The extensive experiments on clothing
change ReID datasets show the out-performance of our methods.
