  Person re-identification (re-ID) plays an important role in applications such
as public security and video surveillance. Recently, learning from synthetic
data, which benefits from the popularity of synthetic data engine, has achieved
remarkable performance. However, in pursuit of high accuracy, researchers in
the academic always focus on training with large-scale datasets at a high cost
of time and label expenses, while neglect to explore the potential of
performing efficient training from millions of synthetic data. To facilitate
development in this field, we reviewed the previously developed synthetic
dataset GPR and built an improved one (GPR+) with larger number of identities
and distinguished attributes. Based on it, we quantitatively analyze the
influence of dataset attribute on re-ID system. To our best knowledge, we are
among the first attempts to explicitly dissect person re-ID from the aspect of
attribute on synthetic dataset. This research helps us have a deeper
understanding of the fundamental problems in person re-ID, which also provides
useful insights for dataset building and future practical usage.
