From Synthetic to Real: Unveiling the Power of Synthetic Data for Video
  Person Re-ID
Xiangqun Zhang Ruize Han Wei Feng
In this paper, we study a new problem of cross-domain video based person
re-identification (Re-ID). Specifically, we take the synthetic video dataset as
the source domain for training and use the real-world videos for testing, which
significantly reduces the dependence on real training data collection and
annotation. To unveil the power of synthetic data for video person Re-ID, we
first propose a self-supervised domain invariant feature learning strategy for
both static and temporal features. Then, to further improve the person
identification ability in the target domain, we develop a mean-teacher scheme
with the self-supervised ID consistency loss. Experimental results on four real
datasets verify the rationality of cross-synthetic-real domain adaption and the
effectiveness of our method. We are also surprised to find that the synthetic
data performs even better than the real data in the cross-domain setting.