Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cut-and-Paste Neural Rendering   Cut-and-paste methods take an object from one image and insert it into
another. Doing so often results in unrealistic looking images because the
inserted object's shading is inconsistent with the target scene's shading.
Existing reshading methods require a geometric and physical model of the
inserted object, which is then rendered using environment parameters.
Accurately constructing such a model only from a single image is beyond the
current understanding of computer vision. We describe an alternative procedure
-- cut-and-paste neural rendering, to render the inserted fragment's shading
field consistent with the target scene. We use a Deep Image Prior (DIP) as a
neural renderer trained to render an image with consistent image decomposition
inferences. The resulting rendering from DIP should have an albedo consistent
with composite albedo; it should have a shading field that, outside the
inserted fragment, is the same as the target scene's shading field; and
composite surface normals are consistent with the final rendering's shading
field. The result is a simple procedure that produces convincing and realistic
shading. Moreover, our procedure does not require rendered images or
image-decomposition from real images in the training or labeled annotations. In
fact, our only use of simulated ground truth is our use of a pre-trained normal
estimator. Qualitative results are strong, supported by a user study comparing
against the state-of-the-art image harmonization baseline.

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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: eess