Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

Abstract

An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally develElectronic supplementary material The online version of this article (http://dx.

Probabilistic Deformable Surface Tracking from Multiple Videos

“…Vlasic et al [1] first optimize for the pose using the visual hull, then refine the shape estimate from the silhouettes. The works by Mundermann, Corraza et al [3,7] use a variant of the ICP algorithm [8] to fit an articulated model to the visual hull. The more generic framework used by Aguiar et al [9] relies on the preservation of Laplacian coordinates of a coarse tetrahedral mesh whose deformation is guided by silhouettes and photometric information.…”

Toward user-specific tracking by detection of human shapes in multi-cameras

“…Experiments demonstrate that this hybrid discriminativegenerative framework leads to better, or comparable results than purely generative approaches, e.g. [6,8], reducing error accumulations and hence increasing the stability. The contribution of this work is two-fold: (1) a one-shot correspondence inference with complete 3D input (rather than 2.5D as in [22,27]), which leads to (2) a hybrid subjectspecific human pose and shape capture method that relies little on former results (unlike other ICP-like methods [6,12,15,18]), holding the ability to recover from drifting.…”

“…Among the vast literature on human pose estimation [19], we focus on top-down approaches that assume a 3D model and deform it according to input data, either directly with pixels as in [12,18,25], or with 3D points as in [6,8,15]. These methods typically decompose into two main steps: (i) data association, where observations are associated to the model, and (ii) deformation estimation, where deformation parameters are estimated given the associations.…”

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