Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose
  and Shape Estimation   Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural Body Fitting (NBF)). It integrates a statistical body model
within a CNN, leveraging reliable bottom-up semantic body part segmentation and
robust top-down body model constraints. NBF is fully differentiable and can be
trained using 2D and 3D annotations. In detailed experiments, we analyze how
the components of our model affect performance, especially the use of part
segmentations as an explicit intermediate representation, and present a robust,
efficiently trainable framework for 3D human pose estimation from 2D images
with competitive results on standard benchmarks. Code will be made available at
http://github.com/mohomran/neural_body_fitting

27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph