Capturing　the　hidden　relationships　in　2D　pose　sequences　is　crucial　for　accurate　3D　human　pose　estimation(HPE).　Recent　studies　have　shown　that　frequency　domain　information,　independent　of　spatio-temporal　information,　has　strong　capabilities　on　representing　the　pose　sequences.　However,　there　are　few　works　exploring　more　appropriate　ways　to　fuse　these　different　kinds　of　information.　In　this　paper,　we　propose　an　alternating　cyclic　approach　for　fusing　spatio-temporal　information　and　frequency　information　to　achieve　accurate　3D　human　pose　estimation.　The　designed　alternating　cyclic　fusion　network　allows　for　a　more　comprehensive　integration　of　different　features,　leading　to　improved　accuracy.　By　leveraging　feature　splitting　and　time-frequency　convolution,　the　existing　features　are　processed　more　appropriately,　and　achieving　model　lightweighting.　Experimental　results　demonstrate　that　our　approach　achieves　comparable　accuracy　to　state-of-the-art　methods　while　significantly　outperforming　mainstream　methods　in　terms　of　model　lightweighting.　In　conclusion,　the　introduction　of　frequency　domain　information　is　of　great　significance　for　pose　estimation　tasks.　©　2024　SPIE.　All　rights　reserved.