Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Dense Correspondence from Synthetic Environments   Estimation of human shape and pose from a single image is a challenging task.
It is an even more difficult problem to map the identified human shape onto a
3D human model. Existing methods map manually labelled human pixels in real 2D
images onto the 3D surface, which is prone to human error, and the sparsity of
available annotated data often leads to sub-optimal results. We propose to
solve the problem of data scarcity by training 2D-3D human mapping algorithms
using automatically generated synthetic data for which exact and dense 2D-3D
correspondence is known. Such a learning strategy using synthetic environments
has a high generalisation potential towards real-world data. Using different
camera parameter variations, background and lighting settings, we created
precise ground truth data that constitutes a wider distribution. We evaluate
the performance of models trained on synthetic using the COCO dataset and
validation framework. Results show that training 2D-3D mapping network models
on synthetic data is a viable alternative to using real data.

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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci