In　recent　years,　the　image　inpainting　technology　based　on　deep　learning　has　made　remarkable　progress,　which　can　better　complete　the　complex　image　inpainting　task　compared　with　traditional　methods.　However,　most　of　the　existing　methods　can　not　generate　reasonable　structure　and　fine　texture　details　at　the　same　time.　To　solve　this　problem,　in　this　paper　we　propose　a　two-stage　image　inpainting　method　with　structure　awareness　based　on　Generative　Adversarial　Networks,　which　divides　the　inpainting　process　into　two　sub　tasks,　namely,　image　structure　generation　and　image　content　generation.　In　the　former　stage,　the　network　generates　the　structural　information　of　the　missing　area;　while　in　the　latter　stage,　the　network　uses　this　structural　information　as　a　prior,　and　combines　the　existing　texture　and　color　information　to　complete　the　image.　Extensive　experiments　are　conducted　to　evaluate　the　performance　of　our　proposed　method　on　Places2,　CelebA　and　Paris　Streetview　datasets.　The　experimental　results　show　the　superior　performance　of　the　proposed　method　compared　with　other　state-of-the-art　methods　qualitatively　and　quantitatively.　©　2021　ACM.