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Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras

Gilbert, Andrew, Hilton, Adrian and Collomosse, John (2020) Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras In: 31st British Machine Vision Conference, 7-11 Sept 2020, London, UK.

BMVC_20_MinCam_Pose_estimation__CameraReady_.pdf - Accepted version Manuscript

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We aim to simultaneously estimate the 3D articulated pose and high fidelity volumetric occupancy of human performance, from multiple viewpoint video (MVV) with as few as two views. We use a multi-channel symmetric 3D convolutional encoder-decoder with a dual loss to enforce the learning of a latent embedding that enables inference of skeletal joint positions and a volumetric reconstruction of the performance. The inference is regularised via a prior learned over a dataset of view-ablated multi-view video footage of a wide range of subjects and actions, and show this to generalise well across unseen subjects and actions. We demonstrate improved reconstruction accuracy and lower pose estimation error relative to prior work on two MVV performance capture datasets: Human 3.6M and TotalCapture.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Department of Music and Media
Authors :
Date : 5 August 2020
Copyright Disclaimer : c 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Additional Information : Embargo OK Metadata pending
Depositing User : James Marshall
Date Deposited : 07 Aug 2020 13:45
Last Modified : 07 Sep 2020 02:08

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