Three-dimensional　human　pose　estimation　is　a　hot　research　topic　in　the　field　of　computer　vision.　Aimed　at　the　lack　of　labels　in　depth　images　and　the　low　generalization　ability　of　models　caused　by　single　human　pose,　this　paper　innovatively　proposes　a　method　of　3D　human　pose　estimation　based　on　multi-source　image　weakly-supervised　learning.　This　method　mainly　includes　the　following　points.　First,　multi-source　image　fusion　training　method　is　used　to　improve　the　generalization　ability　of　the　model.　Second,　weakly-supervised　learning　approach　is　proposed　to　solve　the　problem　of　label　insufficiency.　Third,　in　order　to　improve　the　attitude　estimation　results,　this　paper　improve　the　design　of　the　residual　module.　The　experimental　results　show　that　the　regression　accuracy　from　our　improved　network　increases　by　0.2%,　and　meanwhile　the　training　time　reduces　by　28%　compared　with　the　original　network.　In　a　word,　the　proposed　method　obtains　excellent　estimation　results　with　both　depth　images　and　color　images.　©　2019,　Editorial　Board　of　JBUAA.　All　right　reserved.