In　this　paper,　we　propose　a　high-resolution　virtual　try-on　network　model　based　on　2D　images,　which　can　seamlessly　put　on　given　clothing　to　a　target　person　with　any　pose.　Under　the　coarse-to-fine　strategy,　we　firstly　transform　the　given　normal　clothes　to　warped　clothes　to　well　match　the　pose　of　the　person　by　a　clothing　matching　module,　then　these　two　generated　images　are　combined　to　generate　one　fitting　image　of　the　person　put　on　the　given　clothes　by　a　try-on　module,　lastly　utilize　a　Very　Deep　Super　Resolution　(VDSR)　module　to　refine　the　generated　fitting　image.　Compared　to　the　3D　based　methods　that　are　computationally　prohibitive,　our　method　only　needs　2D　images,　which　is　much　faster.　We　evaluate　our　proposed　model　both　quantitatively　(i.e.,　in　terms　of　SSIM)　and　qualitatively　on　a　public　virtual　try-on　dataset　(i.e,　Zalando).　The　experimental　results　demonstrate　the　effectiveness　of　the　proposed　method:　generating　visually　better　quality　of　images,　our　new　method　can　improve　the　SSIM　by　1.5%.　©　Published　under　licence　by　IOP　Publishing　Ltd.