Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Performance Capture from Monocular Video in the Wild   Capturing the dynamically deforming 3D shape of clothed human is essential
for numerous applications, including VR/AR, autonomous driving, and
human-computer interaction. Existing methods either require a highly
specialized capturing setup, such as expensive multi-view imaging systems, or
they lack robustness to challenging body poses. In this work, we propose a
method capable of capturing the dynamic 3D human shape from a monocular video
featuring challenging body poses, without any additional input. We first build
a 3D template human model of the subject based on a learned regression model.
We then track this template model's deformation under challenging body
articulations based on 2D image observations. Our method outperforms
state-of-the-art methods on an in-the-wild human video dataset 3DPW. Moreover,
we demonstrate its efficacy in robustness and generalizability on videos from
iPER datasets.

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The categories of this paper are: nlin
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: stat
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph