Multiple　views　play　a　vital　role　in　3D　pose　estimation　tasks.　Ideally,　multi-view　3D　pose　estimation　tasks　should　directly　utilize　naturally　collected　videos　for　pose　estimation.　However,　due　to　the　constraints　of　video　synchronization,　existing　methods　often　use　expensive　hardware　devices　to　synchronize　the　initiation　of　cameras,　which　restricts　most　3D　pose　collection　scenarios　to　indoor　settings.　Some　recent　works　learn　deep　neural　networks　to　align　desynchronized　datasets　derived　from　synchronized　cameras　and　can　only　produce　frame-level　accuracy.　For　fractional　frame　video　synchronization,　this　work　proposes　an　Inter-Frame　and　Intra-Frame　Desynchronized　Dataset　(IFID),　which　labels　fractional　time　intervals　between　two　video　clips.　IFID　is　the　first　dataset　that　annotates　inter-frame　and　intra-frame　intervals,　with　a　total　of　382,　500　video　clips　annotated,　making　it　the　largest　dataset　to　date.　We　also　develop　a　novel　model　based　on　the　Transformer　architecture,　named　InSynFormer,　for　synchronizing　inter-frame　and　intra-frame.　Extensive　experimental　evaluations　demonstrate　its　promising　performance.　The　dataset　and　source　code　of　the　model　are　available　at　https://github.com/yuxuan-cser/InSynFormer.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.