  We study the problem of synthesizing immersive 3D indoor scenes from one or
more images. Our aim is to generate high-resolution images and videos from
novel viewpoints, including viewpoints that extrapolate far beyond the input
images while maintaining 3D consistency. Existing approaches are highly
complex, with many separately trained stages and components. We propose a
simple alternative: an image-to-image GAN that maps directly from reprojections
of incomplete point clouds to full high-resolution RGB-D images. On the
Matterport3D and RealEstate10K datasets, our approach significantly outperforms
prior work when evaluated by humans, as well as on FID scores. Further, we show
that our model is useful for generative data augmentation. A
vision-and-language navigation (VLN) agent trained with trajectories
spatially-perturbed by our model improves success rate by up to 1.5% over a
state of the art baseline on the R2R benchmark. Our code will be made available
to facilitate generative data augmentation and applications to downstream
robotics and embodied AI tasks.
