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arXiv · 1807.01950

Volumetric performance capture from minimal camera viewpoints

Andrew Gilbert·Marco Volino·John Collomosse·Adrian Hilton
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Abstract

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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Andrew Gilbert, Marco Volino, John Collomosse, Adrian Hilton. 2018-07-05. Volumetric performance capture from minimal camera viewpoints. https://arxiv.org/abs/1807.01950

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