Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare   We present DenseRaC, a novel end-to-end framework for jointly estimating 3D
human pose and body shape from a monocular RGB image. Our two-step framework
takes the body pixel-to-surface correspondence map (i.e., IUV map) as proxy
representation and then performs estimation of parameterized human pose and
shape. Specifically, given an estimated IUV map, we develop a deep neural
network optimizing 3D body reconstruction losses and further integrating a
render-and-compare scheme to minimize differences between the input and the
rendered output, i.e., dense body landmarks, body part masks, and adversarial
priors. To boost learning, we further construct a large-scale synthetic dataset
(MOCA) utilizing web-crawled Mocap sequences, 3D scans and animations. The
generated data covers diversified camera views, human actions and body shapes,
and is paired with full ground truth. Our model jointly learns to represent the
3D human body from hybrid datasets, mitigating the problem of unpaired training
data. Our experiments show that DenseRaC obtains superior performance against
state of the art on public benchmarks of various humanrelated tasks.

21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas