  The main difficulty of person re-identification (ReID) lies in collecting
annotated data and transferring the model across different domains. This paper
presents UnrealPerson, a novel pipeline that makes full use of unreal image
data to decrease the costs in both the training and deployment stages. Its
fundamental part is a system that can generate synthesized images of
high-quality and from controllable distributions. Instance-level annotation
goes with the synthesized data and is almost free. We point out some details in
image synthesis that largely impact the data quality. With 3,000 IDs and
120,000 instances, our method achieves a 38.5% rank-1 accuracy when being
directly transferred to MSMT17. It almost doubles the former record using
synthesized data and even surpasses previous direct transfer records using real
data. This offers a good basis for unsupervised domain adaption, where our
pre-trained model is easily plugged into the state-of-the-art algorithms
towards higher accuracy. In addition, the data distribution can be flexibly
adjusted to fit some corner ReID scenarios, which widens the application of our
pipeline. We will publish our data synthesis toolkit and synthesized data in
https://github.com/FlyHighest/UnrealPerson.

Computer Vision and Pattern Recognition