  We propose a semantically-aware novel paradigm to perform image extrapolation
that enables the addition of new object instances. All previous methods are
limited in their capability of extrapolation to merely extending the already
existing objects in the image. However, our proposed approach focuses not only
on (i) extending the already present objects but also on (ii) adding new
objects in the extended region based on the context. To this end, for a given
image, we first obtain an object segmentation map using a state-of-the-art
semantic segmentation method. The, thus, obtained segmentation map is fed into
a network to compute the extrapolated semantic segmentation and the
corresponding panoptic segmentation maps. The input image and the obtained
segmentation maps are further utilized to generate the final extrapolated
image. We conduct experiments on Cityscapes and ADE20K-bedroom datasets and
show that our method outperforms all baselines in terms of FID, and similarity
in object co-occurrence statistics.
