Recently,　CNN-Transformer　hybrid　network　has　been　proposed　to　resolve　either　the　heavy　computational　burden　of　CNN　or　the　difficulty　encountered　during　training　the　Transformer-based　networks.　In　this　work,　we　design　an　efficient　and　effective　CNN-Transformer　hybrid　network　for　human　pose　estimation,　namely　CTHPose.　Specifically,　Polarized　CNN　Module　is　employed　to　extract　the　feature　with　plentiful　visual　semantic　clues,　which　is　beneficial　for　the　convergence　of　the　subsequent　Transformer　encoders.　Pyramid　Transformer　Module　is　utilized　to　build　the　long-term　relationship　between　human　body　parts　with　lightweight　structure　and　less　computational　complexity.　To　establish　long-term　relationship,　large　field　of　view　is　necessary　in　Transformer,　which　leads　to　a　large　computational　workload.　Hence,　instead　of　the　entire　feature　map,　we　introduced　a　reorganized　small　sliding　window　to　provide　the　required　large　field　of　view.　Finally,　Heatmap　Generator　is　designed　to　reconstruct　the　2D　heatmaps　from　the　1D　keypoint　representation,　which　balances　parameters　and　FLOPs　while　obtaining　accurate　prediction.　According　to　quantitative　comparison　experiments　with　CNN　estimators,　CTHPose　significantly　reduces　the　number　of　network　parameters　and　GFLOPs,　while　also　providing　better　detection　accuracy.　Compared　with　mainstream　pure　Transformer　networks　and　state-of-the-art　CNN-Transformer　hybrid　networks,　this　network　also　has　competitive　performance,　and　is　more　robust　to　the　clothing　pattern　interference　and　overlapping　limbs　from　the　visual　perspective.