Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structured Local Radiance Fields for Human Avatar Modeling   It is extremely challenging to create an animatable clothed human avatar from
RGB videos, especially for loose clothes due to the difficulties in motion
modeling. To address this problem, we introduce a novel representation on the
basis of recent neural scene rendering techniques. The core of our
representation is a set of structured local radiance fields, which are anchored
to the pre-defined nodes sampled on a statistical human body template. These
local radiance fields not only leverage the flexibility of implicit
representation in shape and appearance modeling, but also factorize cloth
deformations into skeleton motions, node residual translations and the dynamic
detail variations inside each individual radiance field. To learn our
representation from RGB data and facilitate pose generalization, we propose to
learn the node translations and the detail variations in a conditional
generative latent space. Overall, our method enables automatic construction of
animatable human avatars for various types of clothes without the need for
scanning subject-specific templates, and can generate realistic images with
dynamic details for novel poses. Experiment show that our method outperforms
state-of-the-art methods both qualitatively and quantitatively.

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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org