3D　hand　pose　estimation　aims　to　infer　the　position　information　of　hand　joints　from　a　single　image,　which　is　widely　applied　to　virtual　reality,　natural　human-computer　interaction,　autonomous　driving　and　various　other　fields.　Some　methods　treat　all　joints　as　a　whole　to　estimate　3D　hand　pose　and　others　divide　the　hand　joints　into　different　groups　to　regress　the　parts　of　hand.　However,　these　methods　don’t　leverage　the　complementarity　between　global　and　local　part　poses.　To　address　this　issue,　this　paper　proposes　a　novel　dual-branch　framework　aiming　at　comprehensively　capturing　information　about　hand　poses.　One　branch　focuses　on　capturing　the　overall　posture　information　of　the　hand,　while　the　other　one　concentrates　on　detailed　description　of　the　palm　joints　and　finger　joints.　This　global-local　dual-branch　structure　addresses　the　limitation　of　previous　methods　and　can　handle　more　complex　gestures.　To　further　enhance　the　association　between　the　dual　branches,　this　paper　proposes　a　novel　online　knowledge　distillation　strategy.　The　proposed　strategy　integrates　the　prediction　results　of　both　branches　to　construct　a　teacher　network　and　then　transfers　the　knowledge　learned　by　the　teacher　network　back　to　two　student　networks.　Therefore,　this　method　can　better　learn　knowledge　that　contains　both　global　and　local　features.　A　series　of　experiments　are　conducted　on　publicly　available　STB　and　RHD　datasets,　and　the　experimental　results　validates　the　effectiveness　of　the　proposed　3D　hand　pose　estimation　method.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2024.