Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
Document Type
Conference Proceeding
Publication Title
MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
Abstract
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.
First Page
3422
Last Page
3430
DOI
10.1145/3394171.3413815
Publication Date
10-12-2020
Keywords
person re-identification, synthesized dataset, unity3d
Recommended Citation
Y. Wang, S. Liao, and L. Shao, "Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification", In Proceedings of the 28th ACM International Conference on Multimedia (MM '20), New York, NY, USA, pp. 3422–3430, Oct 2020. doi:10.1145/3394171.3413815
Additional Links
DOI link: https://doi.org/10.1145/3394171.3413815
Comments
IR conditions: non-described