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Volumetric performance capture from minimal camera viewpoints

Gilbert, Andrew, Volino, Marco, Collomosse, John and Hilton, Adrian (2018) Volumetric performance capture from minimal camera viewpoints In: ECCV 2018: European Conference on Computer Vision, 08-14 Sep 2018, Munich, Germany.

Volumetric performance capture from minimal camera viewpoints.pdf - Accepted version Manuscript

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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 prob- abilistic 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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Department of Music and Media
Authors :
Editors :
Ferrari, V
Hebert, M
Sminchisescu, C
Weiss, Y
Date : 6 October 2018
DOI : 10.1007/978-3-030-01252-6_35
Copyright Disclaimer : © 2018 the authors
Related URLs :
Depositing User : Clive Harris
Date Deposited : 25 Jul 2018 13:35
Last Modified : 07 Oct 2019 02:08

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