Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Volumetric performance capture from minimal camera viewpoints   We present a convolutional autoencoder that enables high fidelity volumetric
reconstructions of human performance to be captured from multi-view video
comprising only a small set of camera views. Our method yields similar
end-to-end reconstruction error to that of a probabilistic visual hull computed
using significantly more (double or more) viewpoints. We use a deep prior
implicitly learned by the autoencoder trained over a dataset of view-ablated
multi-view video footage of a wide range of subjects and actions. This opens up
the possibility of high-end volumetric performance capture in on-set and
prosumer scenarios where time or cost prohibit a high witness camera count.

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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: eess