Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RIN: Textured Human Model Recovery and Imitation with a Single Image   Human imitation has become topical recently, driven by GAN's ability to
disentangle human pose and body content. However, the latest methods hardly
focus on 3D information, and to avoid self-occlusion, a massive amount of input
images are needed. In this paper, we propose RIN, a novel volume-based
framework for reconstructing a textured 3D model from a single picture and
imitating a subject with the generated model. Specifically, to estimate most of
the human texture, we propose a U-Net-like front-to-back translation network.
With both front and back images input, the textured volume recovery module
allows us to color a volumetric human. A sequence of 3D poses then guides the
colored volume via Flowable Disentangle Networks as a volume-to-volume
translation task. To project volumes to a 2D plane during training, we design a
differentiable depth-aware renderer. Our experiments demonstrate that our
volume-based model is adequate for human imitation, and the back view can be
estimated reliably using our network. While prior works based on either 2D pose
or semantic map often fail for the unstable appearance of a human, our
framework can still produce concrete results, which are competitive to those
imagined from multi-view input.

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The categories of this paper are: q-fin
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn