The　introduction　of　multiple　viewpoints　in　video　scenes　inevitably　increases　the　bitrates　required　for　storage　and　transmission.　To　reduce　bitrates,　researchers　have　developed　methods　to　skip　intermediate　viewpoints　during　compression　and　delivery,　and　ultimately　reconstruct　them　using　Side　Information　(SInfo).　Typically,　depth　maps　are　used　to　construct　SInfo.　However,　these　methods　suffer　from　reconstruction　inaccuracies　and　inherently　high　bitrates.　In　this　paper,　we　propose　a　novel　multi-view　video　coding　method　that　leverages　the　image　generation　capabilities　of　Generative　Adversarial　Network　(GAN)　to　improve　the　reconstruction　accuracy　of　SInfo.　Additionally,　we　consider　incorporating　information　from　adjacent　temporal　and　spatial　viewpoints　to　further　reduce　SInfo　redundancy.　At　the　encoder,　we　construct　a　spatio-temporal　Epipolar　Plane　Image　(EPI)　and　further　utilize　a　convolutional　network　to　extract　the　latent　code　of　a　GAN　as　SInfo.　At　the　decoder,　we　combine　the　SInfo　and　adjacent　viewpoints　to　reconstruct　intermediate　views　using　the　GAN　generator.　Specifically,　we　establish　a　joint　encoder　constraint　for　reconstruction　cost　and　SInfo　entropy　to　achieve　an　optimal　trade-off　between　reconstruction　quality　and　bitrate　overhead.　Experiments　demonstrate　the　significant　improvement　in　Rate–Distortion　(RD)　performance　compared　to　state-of-the-art　methods.　©　2024