  Recently, GAN based method has demonstrated strong effectiveness in
generating augmentation data for person re-identification (ReID), on account of
its ability to bridge the gap between domains and enrich the data variety in
feature space. However, most of the ReID works pick all the GAN generated data
as additional training samples or evaluate the quality of GAN generation at the
entire data set level, ignoring the image-level essential feature of data in
ReID task. In this paper, we analyze the in-depth characteristics of ReID
sample and solve the problem of "What makes a GAN-generated image good for
ReID". Specifically, we propose to examine each data sample with id-consistency
and diversity constraints by mapping image onto different spaces. With a
metric-based sampling method, we demonstrate that not every GAN-generated data
is beneficial for augmentation. Models trained with data filtered by our
quality evaluation outperform those trained with the full augmentation set by a
large margin. Extensive experiments show the effectiveness of our method on
both supervised ReID task and unsupervised domain adaptation ReID task.
