Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Photometry-guided Visual Attribute Transfer   We present a deep learning-based method for propagating spatially-varying
visual material attributes (e.g. texture maps or image stylizations) to larger
samples of the same or similar materials. For training, we leverage images of
the material taken under multiple illuminations and a dedicated data
augmentation policy, making the transfer robust to novel illumination
conditions and affine deformations. Our model relies on a supervised
image-to-image translation framework and is agnostic to the transferred domain;
we showcase a semantic segmentation, a normal map, and a stylization. Following
an image analogies approach, the method only requires the training data to
contain the same visual structures as the input guidance. Our approach works at
interactive rates, making it suitable for material edit applications. We
thoroughly evaluate our learning methodology in a controlled setup providing
quantitative measures of performance. Last, we demonstrate that training the
model on a single material is enough to generalize to materials of the same
type without the need for massive datasets.

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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
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The categories of this paper are: stat