Person re-identification is a hot topic because of its widespread applications in video surveillance and public security. However, it remains a challenging task because of drastic variations in illumination or background across surveillance cameras, which causes the current methods can not work well in real-world scenarios. In addition, due to the scarce dataset, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic random scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose a novel unsupervised Re-ID method via domain adaptation, which can exploit the synthetic data to boost the performance of re-identification in a completely unsupervised way, and free humans from heavy data annotations. Extensive experiments show that our proposed method achieves the state-of-the-art performance on two benchmark datasets, and is very competitive with current cross-domain Re-ID method.