Pose transfer generation with semantic parsing attention network for person re-identification

Abstract

Pose variation as one of the key factors prevents the network from learning a robust person re-identification (Re-ID) model. We in this paper propose a novel Semantic Parsing Attention Network (SPAN) to transfer person pose from the source to another. SPAN is constructed with several Semantic Parsing Attention Blocks. Each block focuses on a local transfer of the human manifold, which can attend to put the sample condition patches to the corresponding location of the target image. The introduction of the binary segmentation mask and the semantic parsing map is not only significant for the seamless stitching of the foreground and the background, but also decreases the computation load considerably. Compared with other methods, our network can characterize better body shape as well as keeping clothing attributes during the pose transfer. And our synthesized image can obtain better appearance and shape consistency related to the source image. Experimental results are provided to show the superiority of our network on both qualitative and quantitative results on Market-1501 and DeepFashion datasets. Furthermore, extensive experiments are also conducted on person Re-ID systems trained with the augmented data, where our network has the ability to improve the person Re-ID accuracy.