  Content creation, central to applications such as virtual reality, can be a
tedious and time-consuming. Recent image synthesis methods simplify this task
by offering tools to generate new views from as little as a single input image,
or by converting a semantic map into a photorealistic image. We propose to push
the envelope further, and introduce Generative View Synthesis (GVS), which can
synthesize multiple photorealistic views of a scene given a single semantic
map. We show that the sequential application of existing techniques, e.g.,
semantics-to-image translation followed by monocular view synthesis, fail at
capturing the scene's structure. In contrast, we solve the semantics-to-image
translation in concert with the estimation of the 3D layout of the scene, thus
producing geometrically consistent novel views that preserve semantic
structures. We first lift the input 2D semantic map onto a 3D layered
representation of the scene in feature space, thereby preserving the semantic
labels of 3D geometric structures. We then project the layered features onto
the target views to generate the final novel-view images. We verify the
strengths of our method and compare it with several advanced baselines on three
different datasets. Our approach also allows for style manipulation and image
editing operations, such as the addition or removal of objects, with simple
manipulations of the input style images and semantic maps respectively. Visit
the project page at https://gvsnet.github.io.
