  Pose-guided person image generation usually involves using paired
source-target images to supervise the training, which significantly increases
the data preparation effort and limits the application of the models. To deal
with this problem, we propose a novel multi-level statistics transfer model,
which disentangles and transfers multi-level appearance features from person
images and merges them with pose features to reconstruct the source person
images themselves. So that the source images can be used as supervision for
self-driven person image generation. Specifically, our model extracts
multi-level features from the appearance encoder and learns the optimal
appearance representation through attention mechanism and attributes
statistics. Then we transfer them to a pose-guided generator for re-fusion of
appearance and pose. Our approach allows for flexible manipulation of person
appearance and pose properties to perform pose transfer and clothes style
transfer tasks. Experimental results on the DeepFashion dataset demonstrate our
method's superiority compared with state-of-the-art supervised and unsupervised
methods. In addition, our approach also performs well in the wild.
