SinMPI: Novel View Synthesis from a Single Image with Expanded
  Multiplane Images
Guo Pu Peng-Shuai Wang Zhouhui Lian
Single-image novel view synthesis is a challenging and ongoing problem that
aims to generate an infinite number of consistent views from a single input
image. Although significant efforts have been made to advance the quality of
generated novel views, less attention has been paid to the expansion of the
underlying scene representation, which is crucial to the generation of
realistic novel view images. This paper proposes SinMPI, a novel method that
uses an expanded multiplane image (MPI) as the 3D scene representation to
significantly expand the perspective range of MPI and generate high-quality
novel views from a large multiplane space. The key idea of our method is to use
Stable Diffusion to generate out-of-view contents, project all scene contents
into an expanded multiplane image according to depths predicted by monocular
depth estimators, and then optimize the multiplane image under the supervision
of pseudo multi-view data generated by a depth-aware warping and inpainting
module. Both qualitative and quantitative experiments have been conducted to
validate the superiority of our method to the state of the art. Our code and
data are available at https://github.com/TrickyGo/SinMPI.