RESEARCH
SynSin: End-to-end View Synthesis from a Single Image
May 13, 2020
Abstract
View synthesis allows for the generation of new views ofa scene given one or more images. This is challenging; it requires comprehensively understanding the 3D scene from images. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task using a single image at test time; it is trained on real images without any ground-truth 3D information. To this end, we introduce a novel differentiable point cloud renderer that is used to transform a latent 3D point cloud of features into the target view. The projected features are de-coded by our refinement network to inpaint missing regions and generate a realistic output image. The 3D component inside of our generative model allows for interpretable manipulation of the latent feature space at test time ,e.g. we can animate trajectories from a single image. Additionally, we can generate high resolution images and generalise to otherinput resolutions. We outperform baselines and prior work on the Matterport, Replica, and RealEstate10K datasets
AUTHORS
Written by
Georgia Gkioxari
Justin Johnson
Rick Szeliski
Olivia Wiles
Publisher
CVPR
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