  Person re-identification has seen significant advancement in recent years.
However, the ability of learned models to generalize to unknown target domains
still remains limited. One possible reason for this is the lack of large-scale
and diverse source training data, since manually labeling such a dataset is
very expensive and privacy sensitive. To address this, we propose to
automatically synthesize a large-scale person re-identification dataset
following a set-up similar to real surveillance but with virtual environments,
and then use the synthesized person images to train a generalizable person
re-identification model. Specifically, we design a method to generate a large
number of random UV texture maps and use them to create different 3D clothing
models. Then, an automatic code is developed to randomly generate various
different 3D characters with diverse clothes, races and attributes. Next, we
simulate a number of different virtual environments using Unity3D, with
customized camera networks similar to real surveillance systems, and import
multiple 3D characters at the same time, with various movements and
interactions along different paths through the camera networks. As a result, we
obtain a virtual dataset, called RandPerson, with 1,801,816 person images of
8,000 identities. By training person re-identification models on these
synthesized person images, we demonstrate, for the first time, that models
trained on virtual data can generalize well to unseen target images, surpassing
the models trained on various real-world datasets, including CUHK03,
Market-1501, DukeMTMC-reID, and almost MSMT17. The RandPerson dataset is
available at https://github.com/VideoObjectSearch/RandPerson.
