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Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging

Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Variou...

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Detalles Bibliográficos
Autores principales: Tits, Mickaël, Tilmanne, Joëlle, Dutoit, Thierry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039011/
https://www.ncbi.nlm.nih.gov/pubmed/29990367
http://dx.doi.org/10.1371/journal.pone.0199744
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author Tits, Mickaël
Tilmanne, Joëlle
Dutoit, Thierry
author_facet Tits, Mickaël
Tilmanne, Joëlle
Dutoit, Thierry
author_sort Tits, Mickaël
collection PubMed
description Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: https://github.com/numediart/MocapRecovery.
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spelling pubmed-60390112018-07-19 Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging Tits, Mickaël Tilmanne, Joëlle Dutoit, Thierry PLoS One Research Article Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: https://github.com/numediart/MocapRecovery. Public Library of Science 2018-07-10 /pmc/articles/PMC6039011/ /pubmed/29990367 http://dx.doi.org/10.1371/journal.pone.0199744 Text en © 2018 Tits et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tits, Mickaël
Tilmanne, Joëlle
Dutoit, Thierry
Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title_full Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title_fullStr Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title_full_unstemmed Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title_short Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
title_sort robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039011/
https://www.ncbi.nlm.nih.gov/pubmed/29990367
http://dx.doi.org/10.1371/journal.pone.0199744
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