In　this　paper,　we　explore　3D　human　pose　estimation　on　the　RGB+D　images.　While　many　researchers　try　to　directly　predict　3D　pose　from　single　RGB　image,　we　propose　a　simple　framework　that　could　predict　3D　pose　predictions　with　the　RGB　image　and　depth　image.　Our　approach　is　based　on　two　aspects.　On　the　one　hand,　we　predicted　accurate　2D　joint　locations　from　RGB　image　by　applying　the　stacked　hourglass　networks　based　on　the　improved　residual　architecture.　On　the　other　hand,　in　view　of　obtained　2D　joint　locations,　we　could　estimate　3D　pose　with　the　depth　after　calculating　depth　image　patches.　In　general,　compared　with　the　state-of-the-art　approaches,　our　model　achieves　signification　improvement　on　benchmark　dataset.