Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Human Body Reconstruction from a Single Image via Volumetric
  Regression   This paper proposes the use of an end-to-end Convolutional Neural Network for
direct reconstruction of the 3D geometry of humans via volumetric regression.
The proposed method does not require the fitting of a shape model and can be
trained to work from a variety of input types, whether it be landmarks, images
or segmentation masks. Additionally, non-visible parts, either self-occluded or
otherwise, are still reconstructed, which is not the case with depth map
regression. We present results that show that our method can handle both pose
variation and detailed reconstruction given appropriate datasets for training.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph