Generative View Synthesis:
From Single-view Semantics to Novel-view Images
Tewodros A. Habtegebrial1,4
Varun Jampani2
Orazio Gallo3
Didier Stricker1,4
1 TU Kaiserslautern 2 Google Research 3 NVIDIA 4 DFKI Kaiserslautern

Input Semantics

GVSNet (Ours)

SPADE[1]+SM[2] SPADE[1]+CVS[3]

Accepted at NeurIPS-2020


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.

Method overview

Supplementary Video


Paper and Supplementary Material

Tewodros Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker.
Generative View Synthesis: From Single-view Semantics to Novel-view Images
Arxiv Preprint, 2020. Link


[1] SPADE: Semantic Image Synthesis with Spatially-Adaptive Normalization, Park et al. link
[2] SM: Stereo Magnification: Learning View Synthesis using Multiplane Images, Zhou et al. link
[3] CVS: Monocular Neural Image Based Rendering with Continuous View Control, Chen et al. link

This project was partially funded by the BMBF project VIDETE(01IW1800).
We thank the SDS department at DFKI Kaiserslautern, for their support with GPU infrastructure.
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