3D　human　dance　generation　from　music　is　an　interesting　and　challenging　task　in　which　the　aim　is　to　estimate　3D　pose　from　visual　and　audio　information.　Existing　methods　only　use　skeleton　information　to　complete　this　task,　which　may　cause　jittering　results.　In　addition,　due　to　lack　of　appropriate　evaluation　metrics　for　this　task,　it　is　difficult　to　evaluate　the　quality　of　the　generated　results.　In　this　paper,　the　authors　explore　multi-modality　dance　generation　networks　through　constructing　the　correspondence　between　the　visual　and　the　audio　cues.　Specifically,　they　propose　a　2D　prediction　module　to　predict　future　frames　by　fusing　visual　and　audio　features.　Moreover,　they　propose　a　3D　conversion　module,　which　is　able　to　generate　the　3D　skeleton　from　the　2D　skeleton.　In　addition,　some　new　human　dance　generation　evaluation　metrics　are　proposed　to　evaluate　the　quality　of　the　generated　results.　Experimental　results　indicate　that　the　proposed　modules　can　meet　the　requirements　of　authenticity　and　diversity.