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Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

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Abstract

This paper presents a new motion capture (MoCap) system, the garment-based motion capture system—GaMoCap. The key feature is the use of an easily wearable garment printed with colour-coded pattern and a generic multicamera setup with standard video cameras. The coded pattern allows a high-density distribution of markers per unit of surface (about 40 markers per 100 cm\(^2\)), avoiding markers-swap errors. The high density of markers reconstructed makes possible a simultaneous reconstruction of shape and motion, which gives several concurrent advantages with respect to the state of the art and providing performances comparable with previous marker-based systems. In particular, we provide effective solutions to counter the soft-tissue artefact which is a common problem for garment-based techniques. This effect is reduced using Point Cluster Technique to filter out the points strongly affected by non-rigid motion. Uncertainty of motion estimation has been experimentally quantified by comparing with a state-of-the-art commercial system and numerically predicted by means of a Monte Carlo Method procedure. The experimental evaluation was performed on three different articulated motions: shoulder, knee and hip flexion-extension. The results shows that for the three motion angles estimated with GaMoCap, the system provides comparable accuracies against a commercial VICON system.

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Notes

  1. http://www.vicon.com/.

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Acknowledgments

The authors would like to thank Patrick Olivier, Guy Schofield and Dave Green of Culture Lab, Newcastle University (http://di.ncl.ac.uk/) for providing the VICON system and the support during data acquisition.

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Authors and Affiliations

  1. Department of Mechanical and Structural Engineering, University of Trento, via Mesiano, 77, 38121, Trento, Italy

    Nicoló Biasi, Mattia Tavernini, Massimo Lunardelli, Alberto Fornaser, Mauro Da Lio & Mariolino De Cecco

  2. Istituto di Scienze e Tecnologie della Cognizione (ISTC), Consiglio Nazionale delle Ricerche (CNR), via alla Cascata, 56/C, 38121, Trento, Italy

    Francesco Setti

  3. Visual Geometry and Modelling Lab (VGM), Pattern Analysis and Computer Vision Department (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Morego, 30, 16163, Genova, Italy

    Alessio Del Bue

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  1. Nicoló Biasi
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  2. Francesco Setti
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  3. Alessio Del Bue
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Correspondence to Nicoló Biasi.

Additional information

The work was supported by the FP7-ICT-2009.7.2, Accessible and Assistive ICT, Grant 247765, IP project VERITAS.

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Biasi, N., Setti, F., Del Bue, A. et al. Garment-based motion capture (GaMoCap): high-density capture of human shape in motion. Machine Vision and Applications 26, 955–973 (2015). https://doi.org/10.1007/s00138-015-0701-2

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