  Collecting and labeling real datasets to train the person search networks not
only requires a lot of time and effort, but also accompanies privacy issues.
The weakly-supervised and unsupervised domain adaptation methods have been
proposed to alleviate the labeling burden for target datasets, however, their
generalization capability is limited. We introduce a novel person search method
based on the domain generalization framework, that uses an automatically
labeled unreal dataset only for training but is applicable to arbitrary unseen
real datasets. To alleviate the domain gaps when transferring the knowledge
from the unreal source dataset to the real target datasets, we estimate the
fidelity of person instances which is then used to train the end-to-end network
adaptively. Moreover, we devise a domain-invariant feature learning scheme to
encourage the network to suppress the domain-related features. Experimental
results demonstrate that the proposed method provides the competitive
performance to existing person search methods even though it is applicable to
arbitrary unseen datasets without any prior knowledge and re-training burdens.
