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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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Correspondence to Nicoló Biasi.

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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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