  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
attracted great attention from the public eyes. However, existing datasets are
limited in quantity, diversity and realisticity, and cannot be efficiently used
for re-ID problem. To address this challenge, we manually construct a
large-scale person dataset named FineGPR with fine-grained attribute
annotations. Moreover, aiming to fully exploit the potential of FineGPR and
promote the efficient training from millions of synthetic data, we propose an
attribute analysis pipeline called AOST, which dynamically learns attribute
distribution in real domain, then eliminates the gap between synthetic and
real-world data and thus is freely deployed to new scenarios. Experiments
conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is
on par with) existing real and synthetic datasets, which suggests its
feasibility for re-ID task and proves the proverbial less-is-more principle.
Our synthetic FineGPR dataset is publicly available at
https://github.com/JeremyXSC/FineGPR.
