With　the　rapid　development　of　deep　learning,　human　pose　estimation　technology　has　made　remarkable　progress　in　recent　years,　but　the　existing　methods　are　still　difficult　to　deal　with　the　common　occlusion　problem.　To　address　this　problem,　a　human　pose　estimation　method　based　on　keypoint-level　occlusion　inference　is　proposed　in　this　paper.　Firstly,　a　baseline　human　pose　estimation　network　is　used　to　obtain　the　noisy　representation　of　each　keypoint　of　human　body　from　images　with　occlusion　noises.　Then,　the　occluded　keypoints　are　estimated　through　the　occlusion　part　prediction　module　to　obtain　the　visibility　vector.　The　occlusion　part　prediction　module　is　proposed　in　this　study,　which　consists　of　two　submodules:　occlusion　part　classification　network　and　visibility　encoder.　The　occlusion　part　classification　network　predicts　the　occlusion　state　of　each　keypoint　of　the　human　body.　Based　on　the　channel　attention　mechanism,　the　visibility　encoder　converts　the　predicted　occlusion　state　into　a　set　of　weight　parameters.　Finally,　the　visibility　vector　and　noise　features　are　fused　by　channel　re-weighting　method　to　obtain　the　keypoint-level　occlusion　aware　features,　which　are　used　to　calculate　the　heatmaps　of　the　keypoints.　Experimental　results　on　MPII　and　LSP(leeds　sports　pose)　datasets　show　that,　compared　with　the　baseline　human　pose　estimation　network,　the　proposed　method　can　better　deal　with　the　occlusion　problem　at　a　small　extra　computational　cost,　and　achieve　better　results　than　existing　state-of-the-art　methods.　©　2022,　Science　Press.　All　right　reserved.