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Fast deformable model-based human performance capture and FVV using consumer-grade RGB-D sensors.

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Title: Fast deformable model-based human performance capture and FVV using consumer-grade RGB-D sensors.
Authors: Alexiadis, Dimitrios S.1 dalexiad@gmail.com, Zioulis, Nikolaos1 nziolus@iti.gr, Zarpalas, Dimitrios1 zarpalas@iti.gr, Daras, Petros1 daras@iti.gr
Source: Pattern Recognition. Jul2018, Vol. 79, p260-278. 19p.
Subjects: Motion capture (Human mechanics), Computer vision, Image reconstruction, Texture mapping, Detectors
Abstract: In this paper, a novel end-to-end system for the fast reconstruction of human actor performances into 3D mesh sequences is proposed, using the input from a small set of consumer-grade RGB-Depth sensors. The proposed framework, by offline pre-reconstructing and employing a deformable actor’s 3D model to constrain the on-line reconstruction process, implicitly tracks the human motion. Handling non-rigid deformation of the 3D surface and applying appropriate texture mapping, it finally produces a dynamic sequence of temporally-coherent textured meshes, enabling realistic Free Viewpoint Video (FVV). Given the noisy input from a small set of low-cost sensors, the focus is on the fast (“quick-post”), robust and fully-automatic performance reconstruction. Apart from integrating existing ideas into a complete end-to-end system, which is itself a challenging task, several novel technical advances contribute to the speed, robustness and fidelity of the system, including a layered approach for model-based pose tracking, the definition and use of sophisticated energy functions, parallelizable on the GPU, as well as a new texture mapping scheme. The experimental results on a large number of challenging sequences, and comparisons with model-based and model-free approaches, demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 128589077
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: In this paper, a novel end-to-end system for the fast reconstruction of human actor performances into 3D mesh sequences is proposed, using the input from a small set of consumer-grade RGB-Depth sensors. The proposed framework, by offline pre-reconstructing and employing a deformable actor’s 3D model to constrain the on-line reconstruction process, implicitly tracks the human motion. Handling non-rigid deformation of the 3D surface and applying appropriate texture mapping, it finally produces a dynamic sequence of temporally-coherent textured meshes, enabling realistic Free Viewpoint Video (FVV). Given the noisy input from a small set of low-cost sensors, the focus is on the fast (“quick-post”), robust and fully-automatic performance reconstruction. Apart from integrating existing ideas into a complete end-to-end system, which is itself a challenging task, several novel technical advances contribute to the speed, robustness and fidelity of the system, including a layered approach for model-based pose tracking, the definition and use of sophisticated energy functions, parallelizable on the GPU, as well as a new texture mapping scheme. The experimental results on a large number of challenging sequences, and comparisons with model-based and model-free approaches, demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.patcog.2018.02.013
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 260
    Subjects:
      – SubjectFull: Motion capture (Human mechanics)
        Type: general
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Image reconstruction
        Type: general
      – SubjectFull: Texture mapping
        Type: general
      – SubjectFull: Detectors
        Type: general
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      – TitleFull: Fast deformable model-based human performance capture and FVV using consumer-grade RGB-D sensors.
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            NameFull: Alexiadis, Dimitrios S.
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            NameFull: Zioulis, Nikolaos
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            NameFull: Zarpalas, Dimitrios
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            NameFull: Daras, Petros
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            – D: 01
              M: 07
              Text: Jul2018
              Type: published
              Y: 2018
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              Value: 79
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            – TitleFull: Pattern Recognition
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