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 attention from both academia and the public eye. However, existing synthetic datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for generalizable re-ID problem. To address this challenge, we construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution. 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 AOST to learn attribute distribution in target domain, then apply style transfer network to eliminate 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 and proves the proverbial less-is-more principle. We hope this fine-grained dataset could advance research towards re-ID in real scenarios.

中文翻译：

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少即是多：从具有细粒度属性的合成数据中学习以进行人员重新识别

人员重新识别（re-ID）在公共安全和视频监控等应用中发挥着重要作用。近期，得益于合成数据引擎的普及，合成数据学习受到了学术界和公众的关注。然而，现有的合成数据集在数量、多样性和现实性方面都存在局限性，无法有效地用于可泛化的 re-ID 问题。为了应对这一挑战，我们构建并标记了一个名为 FineGPR 的具有细粒度属性分布的大规模合成人物数据集。此外，为了充分发挥 FineGPR 的潜力并促进数百万合成数据的高效训练，我们提出了一个属性分析管道 AOST 来学习目标域中的属性分布，然后应用样式传输网络来消除合成数据和现实世界数据之间的差距，从而可以自由地部署到新的场景中。在基准上进行的实验表明，使用 AOST 的 FineGPR 优于（或与现有的真实数据集和合成数据集相当），这表明其重新识别的可行性并证明了众所周知的少即是多原则。我们希望这个细粒度的数据集可以推动对真实场景中 re-ID 的研究。