Video　frame　interpolation　has　been　extensively　explored　and　demonstrated,　yet　its　application　to　polarization　remains　largely　unexplored.　Due　to　the　selective　transmission　of　light　by　polarized　filters,　longer　exposure　times　are　typically　required　to　ensure　sufficient　light　intensity,　which　consequently　lower　the　temporal　sample　rates.　Furthermore,　because　polarization　reflected　by　objects　varies　with　shooting　perspective,　focusing　solely　on　estimating　pixel　displacement　is　insufficient　to　accurately　reconstruct　the　intermediate　polarization.　To　tackle　these　challenges,　this　study　proposes　a　deep　learning　method　called　Swin-VFI　based　on　the　Swin-Transformer　and　introduces　a　tailored　loss　function　to　facilitate　the　network＇s　understanding　of　polarization　changes.　To　ensure　the　practicality　of　our　proposed　method,　this　study　evaluates　its　interpolated　frames　in　Shape　from　Polarization　and　Human　Shape　Reconstruction　tasks,　comparing　them　with　other　state-of-the-art　methods　such　as　CAIN,　FLAVR,　and　VFIT.　The　experimental　results　show　that　our　method　achieves　an　average　PSNR　improvement　of　0.43　dB　over　the　suboptimal　methods　in　all　tasks.