  Human pose transfer, which aims at transferring the appearance of a given
person to a target pose, is very challenging and important in many
applications. Previous work ignores the guidance of pose features or only uses
local attention mechanism, leading to implausible and blurry results. We
propose a new human pose transfer method using a generative adversarial network
(GAN) with simplified cascaded blocks. In each block, we propose a pose-guided
non-local attention (PoNA) mechanism with a long-range dependency scheme to
select more important regions of image features to transfer. We also design
pre-posed image-guided pose feature update and post-posed pose-guided image
feature update to better utilize the pose and image features. Our network is
simple, stable, and easy to train. Quantitative and qualitative results on
Market-1501 and DeepFashion datasets show the efficacy and efficiency of our
model. Compared with state-of-the-art methods, our model generates sharper and
more realistic images with rich details, while having fewer parameters and
faster speed. Furthermore, our generated images can help to alleviate data
insufficiency for person re-identification.
