3D　reconstruction　has　been　applied　to　many　research　fields　such　as　robots　and　computer　vision　with　the　fast　development　of　technology.　Despite　significant　progress,　current　3D　human　pose　and　shape　estimation　methods　still　remain　challenge　to　recovery　3D　human　mesh　under　occlusions.　Previous　works　use　a　Iterative　Error　Feedback　(IEF)　loop　to　construct　the　regressor　and　often　have　disregarded　information　at　occluded　regions　that　make　them　difficult　to　handle　occlusions.　However,　we　argue　that　occluded　regions　have　strong　correlations　with　human　body　so　that　they　can　offer　effective　information　for　3D　human　pose　and　shape　estimation.　To　address　this,　we　propose　a　multi-scale　feature　injection　network　MFINet,　that　utilizes　the　information　at　occluded　regions　as　a　secondary　clews　to　enrich　the　image　features　in　a　coarse-to-fine　manner.　In　MFInet,　given　the　image　feature　at　current　scale,　a　Transformer-based　module,　called　feature　inject　transformer　module　(FIM)　is　used　to　inject　human　feature　into　occluded　region　by　considering　their　correlation.　To　this　end,　experiments　show　that　our　method　is　effective　in　both　object　and　subject　results　on　several　benchmarks　including　Human3.6M,　3DPW,　LSP　and　COCO.