Different　from　image-based　3D　pose　estimation,　video-based　3D　pose　estimation　gains　performance　improvement　with　temporal　information.　However,　these　methods　still　face　the　challenge　of　insufficient　generalization　ability,　including　human　motion　speed,　body　shape,　and　camera　distance.　To　address　the　above　problems,　we　propose　a　novel　approach,　referred　to　as　joint　Spatial–temporal　Multi-scale　Transformers　and　Pose　Transformation　Equivalence　Constraints　(SMT-PTEC)　for　3D　human　pose　estimation　from　videos.　We　design　a　more　general　spatial–temporal　multi-scale　feature　extraction　strategy,　and　introduce　optimization　constraints　that　adapt　to　the　diversity　of　data　to　improve　the　accuracy　of　pose　estimation.　Specifically,　we　first　introduce　a　spatial　multi-scale　transformer　to　extract　multi-scale　features　of　pose　and　establish　a　cross-scale　information　transfer　mechanism,　which　effectively　explores　the　underlying　knowledge　of　human　motion.　Then,　we　present　a　temporal　multi-scale　transformer　to　explore　multi-scale　dependencies　between　frames,　enhance　the　adaptability　of　the　network　to　human　motion　speed,　and　improve　the　estimation　accuracy　through　a　context　aware　fusion　of　multi-scale　predictions.　Moreover,　we　add　pose　transformation　equivalence　constraints　by　changing　the　training　samples　with　horizontal　flipping,　scaling,　and　body　shape　transformation　to　effectively　overcome　the　influence　of　camera　distance　and　body　shape　for　the　prediction　accuracy.　Extensive　experimental　results　demonstrate　that　our　approach　achieves　superior　performance　with　less　computational　complexity　than　previous　state-of-the-art　methods.　Code　is　available　at　https://github.com/JNGao123/SMT-PTEC.　©　2024　Elsevier　Inc.