s.InSec.4.3,weshowthatusingthetemporallatentﬂowproducedbyDMtrainedintheoriginaldomain,LFDMcananimatefa-cialimagesfromanewdomainandgeneratebetterspatialdetailsiftheimagedecoderisﬁnetuned.Duringinference,asFig2shows,weﬁrstadopttheDMtrainedinstagetwotogeneratealatentﬂowsequenceˆfK1conditionedonyandgivenimagex0.Togeneratetheoc-cludedregionsinnewframes,theDMalsoproducesanoc-clusionmapsequenceˆmK1.Thenimagex0iswarpedwithˆfK1andˆmK1inthelatentspaceframe-by-frametogeneratethevideoˆxK1.Bykeepingwarpingthegivenimagex0in-steadofprevioussynthesizedframes,wecanavoidartifactaccumulation.MoredetailswillbeintroducedinSec.3.3.Ourcontributionsaresummarizedasfollows:•Weproposenovellatentﬂowdiffusionmodels(LFDM)toachieveimage-to-videogenerationbysyn-thesizingatemporally-coherentﬂowsequenceinthelatentspacebasedonthegivenconditiontowarpthegivenimage.Tothebestofourknowledge,wearetheﬁrsttoapplydiffusionmodelstogeneratelatentﬂowforconditionalimage-to-videotasks.•Anoveltwo-stagetrainingstrategyisproposedforLFDM
Conditional Image-to-Video Generation with Latent Flow Diffusion Models