Due　to　the　complexity　of　real　optical　flow　capture,　the　existing　research　still　has　not　performed　real　optical　flow　capture　of　infrared　(IR)　images　with　the　production　of　an　optical　flow　based　on　IR　images,　which　makes　the　research　and　application　of　deep　learning-based　optical　flow　computation　limited　to　the　field　of　RGB　images　only.　Therefore,　in　this　paper,　we　propose　a　method　to　produce　an　optical　flow　dataset　of　IR　images.　We　utilize　the　RGB-IR　cross-modal　image　transformation　network　to　rationally　transform　existing　RGB　image　optical　flow　datasets.　The　RGB-IR　cross-modal　image　transformation　is　based　on　the　improved　Pix2Pix　implementation,　and　in　the　experiments,　the　network　is　validated　and　evaluated　using　the　RGB-IR　aligned　bimodal　dataset　M3FD.　Then,　RGB-IR　cross-modal　transformation　is　performed　on　the　existing　RGB　optical　flow　dataset　KITTI,　and　the　optical　flow　computation　network　is　trained　using　the　IR　images　generated　by　the　transformation.　Finally,　the　computational　results　of　the　optical　flow　computation　network　before　and　after　training　are　analyzed　based　on　the　RGB-IR　aligned　bimodal　data.