Pose　estimation　based　on　visual　images　is　evolving,　but　it　is　also　limited　by　environmental　factors　such　as　occlusion　and　darkness.　Due　to　its　non-intrusive　and　ubiquitous　characters,　WiFi　Channel　State　Information　(CSI)-based　human　activity　recognition　attracts　immense　attention.　In　this　paper,　a　CSI-based　passive　sensing　system　is　proposed　to　predict　joint　points　of　human　skeleton　for　activity　recognition.　The　system　leverages　a　pair　of　ESP32　based　CSI　sensors　with　bidirectional　link　that　can　prevent　unidirectional　link　from　missing　important　activity　information　to　collect　the　amplitude　of　CSI　signals.　The　Kinect　2.0　is　employed　to　obtain　skeleton　data　as　ground　truth　label　synchronously.　A　hybrid　deep　neural　network　composed　of　Convolutional　Neural　Network　(CNN)　and　Long　Short　Term　Memory　(LSTM)　is　utilized　to　extract　features　of　CSI　signal　and　map　to　corresponding　human　skeleton.　K-means　clustering　algorithm　is　incorporated　to　cull　the　outliers.　Experimental　results　demonstrate　that　the　proposed　system　achieves　satisfactory　results　with　3.489%　average　error.