erialsforCVPR’23PaperTitled“ConditionalImage-to-VideoGenerationwithLatentFlowDiffusionModels”A1.PotentialNegativeSocialImpactConditionalimage-to-videomodelscanbeusedforun-ethicalpurposes[8],e.g.,creatingvideosofcelebritiesforfakenewsspreading.Wewillrestricttheusageofourmod-elstoresearchpurposesonly.Wealsoplantoinvestigatesomefakevideodetectiontechniques[1]thatmaybeeffec-tiveindetectingfakevideosliketheonesgeneratedbyourmethods.A2.AdditionalExperimentsA2.1.AdditionalAblationStudyonNetworkAr-chitectureToevaluatetheperformancedifferenceofourproposedLFDMwithdifferentarchitectures,wechangethedepthoftheimagedecoderΩinstage-oneLFAE(TableA1)andthe3DU-Net(cid:15)θinstage-twoDM(TableA2).WeexperimentwithdifferentsettingsonMUGdatasettogeneratevideosof128×128frameresolution.Inourdefaultsetting,theimagedecoderΩinstage-oneLFAEisimplementedwithanetworkincluding6residualblocksand2up-samplingblocks.InTableA1,wecompareusingdifferentnetworkdepthsfortheimagedecoderΩinstage-oneLFAE.Weaddfourextraresidualblo
Conditional Image-to-Video Generation with Latent Flow Diffusion Models