Photo-realistic　image　synthesis　is　an　attracting　idea　for　person　re-identification　(ReID)　and　data　augmentation　on　human　pose　estimation.　However,　existing　advances　manipulating　human　image　synthesis　lack　texture　details　for　varying　poses　or　appearances.　This　paper　presents　a　person　image　synthesis　Siamese　generative　adversarial　network　(PS2GAN),　which　re-synthesizes　person　image　by　changing　the　pose　of　that　person　to　a　given　pose,　modeled　in　a　Siamese　structure　with　image　generative　network　and　pair　conditional　discriminative　networks　in　single-branch.　For　pose　transfer,　the　proposed　PS2GAN　adopts　Siamese　structure　consisting　of　two　image　generative　networks　and　a　novel　contrastive-pose　loss　regularizing　the　generation　process.　Additionally,　a　nearest-neighbor　loss　computes　the　difference　between　fake　and　real　images　to　make　high-level　information　closer.　Furthermore,　the　proposed　PS2GAN　is　competitive　to　the　state-of-the-art　performance　on　Market-1501　and　DeepFashion　datasets　via　qualitatively　and　quantitatively　comparing　with　prior　works,　and　synthetic　images　of　the　PS2GAN　can　alleviate　data　insufficiency　for　person　ReID.　(c)　2020　Elsevier　B.V.　All　rights　reserved.