  We present a novel approach to view synthesis using multiplane images (MPIs).
Building on recent advances in learned gradient descent, our algorithm
generates an MPI from a set of sparse camera viewpoints. The resulting method
incorporates occlusion reasoning, improving performance on challenging scene
features such as object boundaries, lighting reflections, thin structures, and
scenes with high depth complexity. We show that our method achieves
high-quality, state-of-the-art results on two datasets: the Kalantari light
field dataset, and a new camera array dataset, Spaces, which we make publicly
available.
