Synthesizing　high　dynamic　range　(HDR)　images　from　multiple　low-dynamic　range　(LDR)　exposures　in　dynamic　scenes　is　challenging.　There　are　two　major　problems　caused　by　the　large　motions　of　foreground　objects.　One　is　the　severe　misalignment　among　the　LDR　images.　The　other　is　the　missing　content　due　to　the　over-/under-saturated　regions　caused　by　the　moving　objects,　which　may　not　be　easily　compensated　for　by　the　multiple　LDR　exposures.　Thus,　it　requires　the　HDR　generation　model　to　be　able　to　properly　fuse　the　LDR　images　and　restore　the　missing　details　without　introducing　artifacts.　To　address　these　two　problems,　we　propose　in　this　paper　a　novel　GAN-based　model,　HDR-GAN,　for　synthesizing　HDR　images　from　multi-exposed　LDR　images.　To　our　best　knowledge,　this　work　is　the　first　GAN-based　approach　for　fusing　multi-exposed　LDR　images　for　HDR　reconstruction.　By　incorporating　adversarial　learning,　our　method　is　able　to　produce　faithful　information　in　the　regions　with　missing　content.　In　addition,　we　also　propose　a　novel　generator　network,　with　a　reference-based　residual　merging　block　for　aligning　large　object　motions　in　the　feature　domain,　and　a　deep　HDR　supervision　scheme　for　eliminating　artifacts　of　the　reconstructed　HDR　images.　Experimental　results　demonstrate　that　our　model　achieves　state-of-the-art　reconstruction　performance　over　the　prior　HDR　methods　on　diverse　scenes.