Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SynSin: End-to-end View Synthesis from a Single Image   Single image view synthesis allows for the generation of new views of a scene
given a single input image. This is challenging, as it requires comprehensively
understanding the 3D scene from a single image. 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; 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 decoded 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. Unlike prior work,
we can generate high resolution images and generalise to other input
resolutions. We outperform baselines and prior work on the Matterport, Replica,
and RealEstate10K datasets.

19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin