论文标题
超越现实世界的来源培训数据：可推广人员重新识别的随机3D字符
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
论文作者
论文摘要
近年来，人重新识别已取得了重大进步。但是，学习模型概括为未知目标域的能力仍然有限。造成这种情况的一个可能原因是缺乏大规模和多样化的源培训数据，因为手动标记这样的数据集非常昂贵且对隐私敏感。为了解决这个问题，我们建议自动合成一个大规模的人重新识别数据集之后的设置类似于实际监视，但使用虚拟环境，然后使用合成的人图像来培训可概括的人的重新识别模型。具体来说，我们设计了一种生成大量随机紫外线纹理图的方法，并使用它们来创建不同的3D服装模型。然后，开发自动代码，以随机生成具有不同衣服，种族和属性的各种不同的3D字符。接下来，我们使用Unity3D模拟了许多不同的虚拟环境，具有类似于实际监视系统的自定义相机网络，并同时导入多个3D字符，并通过相机网络沿着不同的路径进行各种运动和交互。结果，我们获得了一个称为Randperson的虚拟数据集，其中有1,801,816个身份的人图像。通过培训这些综合人图像的培训人员重新识别模型，我们首次证明了对虚拟数据训练的模型可以很好地推广到看不见的目标图像，超过了在各种现实世界数据集中训练的模型，包括Cuhk03，Market-1501，Dukemtmc-Reid，几乎是MSMMT17。 randperson数据集可从https://github.com/videoobjectsearch/randperson获得。
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.
