Synthesizing　novel　views　from　a　single　view　image　is　a　highly　ill-posed　problem.　We　discover　an　effective　solution　to　reduce　the　learning　ambiguity　by　expanding　the　single-view　view　synthesis　problem　to　a　multi-view　setting.　Specifically,　we　leverage　the　reliable　and　explicit　stereo　prior　to　generate　a　pseudo-stereo　viewpoint,　which　serves　as　an　auxiliary　input　to　construct　the　3D　space.　In　this　way,　the　challenging　novel　view　synthesis　process　is　decoupled　into　two　simpler　problems　of　stereo　synthesis　and　3D　reconstruction.　In　order　to　synthesize　a　structurally　correct　and　detail-preserved　stereo　image,　we　propose　a　self-rectified　stereo　synthesis　to　amend　erroneous　regions　in　an　identify-rectify　manner.　Hard-to-train　and　incorrect　warping　samples　are　first　discovered　by　two　strategies,　(1)　pruning　the　network　to　reveal　low-confident　predictions;　and　(2)　bidirectionally　matching　between　stereo　images　to　allow　the　discovery　of　improper　mapping.　These　regions　are　then　inpainted　to　form　the　final　pseudo-stereo.　With　the　aid　of　this　extra　input,　a　preferable　3D　reconstruction　can　be　easily　obtained,　and　our　method　can　work　with　arbitrary　3D　representations.　Extensive　experiments　show　that　our　method　outperforms　state-of-the-art　single-view　view　synthesis　methods　and　stereo　synthesis　methods.