3D　human　body　shape　and　pose　reconstructing　from　a　single　RGB　image　is　a　challenging　task　in　the　field　of　computer　vision　and　computer　graphics.　Since　occlusions　are　prevalent　in　real　application　scenarios,　it＇s　important　to　develop　3D　human　body　reconstruction　algorithms　with　occlusions.　However,　existing　methods　didn＇t　take　this　problem　into　account.　In　this　paper,　we　present　a　novel　depth　estimation　Neural　Network,　named　Detailed　Human　Depth　Network(DHDNet),　which　aims　to　reconstruct　the　detailed　and　completed　depth　map　from　a　single　RGB　image　contains　occlusions　of　human　body.　Inspired　by　the　previous　works　[1,　2],　we　propose　an　end-to-end　method　to　obtain　the　fine　detailed　3D　human　mesh.　The　proposed　method　follows　a　coarse-to-fine　refinement　scheme.　Using　the　depth　information　generated　from　DHDNet,　the　coarse　3D　mesh　can　recover　detailed　spatial　structure,　even　the　part　behind　occlusions.　We　also　construct　DepthHuman,　a　2D　in-the-wild　human　dataset　containing　over　18000　synthetic　human　depth　maps　and　corresponding　RGB　images.　Extensive　experimental　results　demonstrate　that　our　approach　has　significant　improvement　in　3D　mesh　reconstruction　accuracy　on　the　occluded　parts.