Motions　in　videos　are　typically　a　mixture　of　local　dynamic　object　motions　and　global　camera　motion,　which　are　inconsistent　in　some　cases,　and　even　interfere　with　each　other,　causing　difficulties　in　various　downstream　applications,　such　as　video　stabilization　that　requires　the　global　motion,　and　action　recognition　that　consumes　local　motions.　Therefore,　it　is　crucial　to　estimate　them　separately.　Existing　methods　separate　two　motions　from　the　mixed　motion　fields,　such　as　optical　flow.　However,　the　quality　of　mixed　motion　determines　the　higher　bounds　of　the　performance.　In　this　work,　we　propose　a　framework,　GLOCAL,　to　directly　estimate　global　and　local　motions　simultaneously　from　adjacent　frames　in　a　self-supervised　manner.　Our　GLOCAL　consists　of　a　Global　Motion　Estimation　(GME)　module　and　a　Local　Motion　Estimation　(LME)　module.　The　GME　module　involves　a　mixed　motion　estimation　backbone,　an　implicit　bottleneck　structure　for　feature　dimension　reduction,　and　an　explicit　bottleneck　for　global　motion　recovery　based　on　the　global　motion　bases　with　foreground　mask　under　the　training　guidance　of　proposed　global　reconstruction　loss.　An　attention　U-Net　is　adopted　for　LME　which　produces　local　motions　while　excluding　motion　of　irrelevant　regions　under　the　guidance　of　proposed　local　reconstruction　loss.　Our　method　can　achieve　better　performance　than　the　existing　methods　on　the　homography　estimation　dataset　DHE　and　the　action　recognition　dataset　NCAA　and　UCF-101.