Human　sensing　based　on　the　low-resolution　infrared　sensor　is　widely　used　in　hand　gestures　recognition,　activity　recognition,　intrusion　detection,　etc.　However,　the　information　about　humans　acquired　by　the　previous　human　sensing　system　using　the　infrared　sensor　is　limited.　In　this　paper,　a　human　pose　estimation　system　is　proposed　to　realize　the　three-dimensional　skeleton　information　acquisition　by　low-resolution　infrared　sensors.　It　is　a　difficult　task　to　acquire　human　pose　estimation　with　more　rich　human　information　from　low-resolution　infrared　sensors.　The　system　leverages　the　8　x　8　pixels　low-resolution　infrared　array　sensor　to　collect　the　activity　data　and　the　Kinect　v2　camera　to　capture　the　three-dimensional　skeleton　of　the　human　body　as　annotations　of　the　infrared　data.　The　convolutional　neural　network-bidirectional　gated　recurrent　unit　model　with　attention　mechanism　(CNN-BiGRU-AM)　model　is　employed　for　model　training　to　effectively　extract　the　characteristics　of　the　infrared　data　from　spatial　and　temporal　dimensions.　The　attention　mechanism　(AM)　can　improve　the　ability　of　the　model　to　capture　important　local　information.　The　bone　joint　point　data　predicted　by　the　model　are　utilized　to　draw　the　three-dimensional　skeleton　diagram.　The　k-means　clustering　algorithm　is　applied　to　eliminate　the　outliers　that　affect　the　overall　visualization　effect　in　the　prediction.　The　accuracy　and　completeness　of　human　pose　estimation　are　measured　by　the　euclidean　distance　between　the　real　coordinates　of　the　bone　joint　points　obtained　by　Kinect　v2　camera　and　the　coordinates　predicted　by　the　model.　The　proportion　of　the　number　of　predictions　with　euclidean　distance　less　than　a　threshold　20　mm　is　90.151%,　representing　the　accuracy　of　human　pose　estimation.　The　experimental　results　show　that　three-dimensional　skeleton　information　can　be　acquired　accurately　by　the　low-resolution　infrared　array　sensor　and　the　subtle　difference　within　each　activity　can　be　observed　through　the　3D　human　pose　to　improve　the　effect　of　activity　recognition.