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

MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation