Texture　transfer　has　been　successfully　applied　in　computer　vision　and　computer　graphics.　Since　non-stationary　textures　are　usually　complex　and　anisotropic,　it　is　challenging　to　transfer　these　textures　by　simple　supervised　method.　In　this　paper,　we　propose　a　general　solution　for　non-stationary　texture　transfer,　which　can　preserve　the　local　structure　and　visual　richness　of　textures.　The　inputs　of　our　framework　are　source　texture　and　semantic　annotation　pair.　We　record　different　semantics　as　different　regions　and　obtain　the　color　and　distribution　information　from　different　regions,　which　is　used　to　guide　the　the　low-level　texture　transfer　algorithm.　Specifically,　we　exploit　these　local　distributions　to　regularize　the　texture　transfer　objective　function,　which　is　minimized　by　iterative　search　and　voting　steps.　In　the　search　step,　we　search　the　nearest　neighbor　fields　of　source　image　to　target　image　through　Generalized　PatchMatch　(GPM)　algorithm.　In　the　voting　step,　we　calculate　histogram　weights　and　coherence　weights　for　different　semantic　regions　to　ensure　color　accuracy　and　texture　continuity,　and　to　further　transfer　the　textures　from　the　source　to　the　target.　By　comparing　with　state-of-the-art　algorithms,　we　demonstrate　the　effectiveness　and　superiority　of　our　technique　in　various　non-stationary　textures.　©　2021　ACM.