In　this　paper,　we　introduce　a　novel　yet　challenging　research　problem,　interactive　crowd　video　generation,　committed　to　producing　diverse　and　continuous　crowd　video,　and　relieving　the　difficulty　of　insufficient　annotated　real-world　datasets　in　crowd　analysis.　Our　goal　is　to　recursively　generate　realistic　future　crowd　video　frames　given　few　context　frames,　under　the　user-specified　guidance,　namely　individual　positions　of　the　crowd.　To　this　end,　we　propose　a　deep　network　architecture　specifically　designed　for　crowd　video　generation　that　is　composed　of　two　complementary　modules,　each　of　which　combats　the　problems　of　crowd　dynamic　synthesis　and　appearance　preservation　respectively.　Particularly,　a　spatio-temporal　transfer　module　is　proposed　to　infer　the　crowd　position　and　structure　from　guidance　and　temporal　information,　and　a　point-aware　flow　prediction　module　is　presented　to　preserve　appearance　consistency　by　flow-based　warping.　Then,　the　outputs　of　the　two　modules　are　integrated　by　a　self-selective　fusion　unit　to　produce　an　identity-preserved　and　continuous　video.　Unlike　previous　works,　we　generate　continuous　crowd　behaviors　beyond　identity　annotations　or　matching.　Extensive　experiments　show　that　our　method　is　effective　for　crowd　video　generation.　More　importantly,　we　demonstrate　the　generated　video　can　produce　diverse　crowd　behaviors　and　be　used　for　augmenting　different　crowd　analysis　tasks,　i.e.,　crowd　counting,　anomaly　detection,　crowd　video　prediction.　Code　is　available　at　https://github.com/Icep2020/CrowdGAN.