Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape
  Reconstruction   Estimating 3D human pose and shape from a single image is highly
under-constrained. To address this ambiguity, we propose a novel prior, namely
kinematic dictionary, which explicitly regularizes the solution space of
relative 3D rotations of human joints in the kinematic tree. Integrated with a
statistical human model and a deep neural network, our method achieves
end-to-end 3D reconstruction without the need of using any shape annotations
during the training of neural networks. The kinematic dictionary bridges the
gap between in-the-wild images and 3D datasets, and thus facilitates end-to-end
training across all types of datasets. The proposed method achieves competitive
results on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP,
while running in real-time given the human bounding boxes.

21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics