  Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to
learn identity information from labeled images in source domains and apply it
to unlabeled images in a target domain. One major issue with many unsupervised
re-identification methods is that they do not perform well relative to large
domain variations such as illumination, viewpoint, and occlusions. In this
paper, we propose a Synthesis Model Bank (SMB) to deal with illumination
variation in unsupervised person re-ID. The proposed SMB consists of several
convolutional neural networks (CNN) for feature extraction and Mahalanobis
matrices for distance metrics. They are trained using synthetic data with
different illumination conditions such that their synergistic effect makes the
SMB robust against illumination variation. To better quantify the illumination
intensity and improve the quality of synthetic images, we introduce a new 3D
virtual-human dataset for GAN-based image synthesis. From our experiments, the
proposed SMB outperforms other synthesis methods on several re-ID benchmarks.
