  Person re-identification (re-ID) is a challenging problem especially when no
labels are available for training. Although recent deep re-ID methods have
achieved great improvement, it is still difficult to optimize deep re-ID model
without annotations in training data. To address this problem, this study
introduces a novel approach for unsupervised person re-ID by leveraging virtual
and real data. Our approach includes two components: virtual person generation
and training of deep re-ID model. For virtual person generation, we learn a
person generation model and a camera style transfer model using unlabeled real
data to generate virtual persons with different poses and camera styles. The
virtual data is formed as labeled training data, enabling subsequently training
deep re-ID model in supervision. For training of deep re-ID model, we divide it
into three steps: 1) pre-training a coarse re-ID model by using virtual data;
2) collaborative filtering based positive pair mining from the real data; and
3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs
and virtual data. The final re-ID model is achieved by iterating between step 2
and step 3 until convergence. Experimental results on two large-scale datasets,
Market-1501 and DukeMTMC-reID, demonstrate the effectiveness of our approach
and shows that the state of the art is achieved in unsupervised person re-ID.
