  Pose variation is one of the key factors which prevents the network from
learning a robust person re-identification (Re-ID) model. To address this
issue, we propose a novel person pose-guided image generation method, which is
called the semantic attention network. The network consists of several semantic
attention blocks, where each block attends to preserve and update the pose code
and the clothing textures. The introduction of the binary segmentation mask and
the semantic parsing is important for seamlessly stitching foreground and
background in the pose-guided image generation. Compared with other methods,
our network can characterize better body shape and keep clothing attributes,
simultaneously. Our synthesized image can obtain better appearance and shape
consistency related to the original image. Experimental results show that our
approach is competitive with respect to both quantitative and qualitative
results on Market-1501 and DeepFashion. Furthermore, we conduct extensive
evaluations by using person re-identification (Re-ID) systems trained with the
pose-transferred person based augmented data. The experiment shows that our
approach can significantly enhance the person Re-ID accuracy.
