Learning robust representations is critical for the success of person re-identification and attribute recognition systems. However, to achieve this, we must use a large dataset of diverse person images as well as annotations of identity labels and/or a set of different attributes. Apart from the obvious concerns about privacy issues, the manual annotation process is both time consuming and too costly. In this paper, we instead propose to use synthetic person images for addressing these difficulties. Specifically, we first introduce Synthetic18K, a large-scale dataset of over 1 million computer generated person images of 18K unique identities with relevant attributes. Moreover, we demonstrate that pretraining of simple deep architectures on Synthetic18K for person re-identification and attribute recognition and then fine-tuning on real data leads to significant improvements in prediction performances, giving results better than or comparable to state-of-the-art models.

学习稳健的表征对于人员重新识别和属性识别系统的成功至关重要。然而，为了实现这一点，我们必须使用大量不同人物图像的数据集以及身份标签和/或一组不同属性的注释。除了对隐私问题的明显担忧之外，手动注释过程既耗时又成本太高。在本文中，我们建议使用合成人物图像来解决这些困难。具体来说，我们首先介绍 Synthetic18K，这是一个包含超过 100 万张计算机生成的具有相关属性的 18K 唯一身份的人物图像的大规模数据集。而且，