ingﬂowtomodelthemotionofﬂuidelements(water,smoke,ﬁre,etc.),theirﬂowmapscanbeeasilyacquiredfrommotiondirectioninputs.Thisisbecauseﬂuidele-mentscanfollowtheassumptionoftime-invariantmotionﬁeld[28],whichdoesnotholdforcomplexmotionssuchashumanmovements.2.2.DiffusionModelsforVideoGenerationDiffusionmodels(DMs)[25,67]haveveryrecentlyfoundremarkablesuccessinimagegeneration[2,15,18,26,32,35,44,51,52,55–57,60,61].Forexample,Rombach[57]proposedlatentdiffusionmodels(LDM)forimagegen-erationbyapplyingDMinthelatentspaceofpretrainedauto-encoders.Forvideogeneration,DMsalsoshowedpromisingperformance[20,24,27,65,74,84].Hoetal.[27]introducedvideodiffusionmodels(VDM)bychang-ingthe2DU-Net[58]inimageDMtobe3DU-Net[12].In[24],theyfurtherutilizedVDMtodesignabasevideogenerationmodelandimproveitwithasequenceofvideosuper-resolutionmodels.Singeretal.[65]exploitedpre-trainedtext-to-imageDMstoachievetext-to-videogenera-tionwithoutpairedtext-videodata.Differentfromalltheabovemodels,LFDMinsteadappliesDMtogeneratelaten
Conditional Image-to-Video Generation with Latent Flow Diffusion Models