  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.
