Browsing by Author "Hilsmann, A."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Markerless Multiview Motion Capture with 3D Shape Model Adaptation
    (© 2019 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2019) Fechteler, P.; Hilsmann, A.; Eisert, P.; Chen, Min and Benes, Bedrich
    In this paper, we address simultaneous markerless motion and shape capture from 3D input meshes of partial views onto a moving subject. We exploit a computer graphics model based on kinematic skinning as template tracking model. This template model consists of vertices, joints and skinning weights learned a priori from registered full‐body scans, representing true human shape and kinematics‐based shape deformations. Two data‐driven priors are used together with a set of constraints and cues for setting up sufficient correspondences. A Gaussian mixture model‐based pose prior of successive joint configurations is learned to soft‐constrain the attainable pose space to plausible human poses. To make the shape adaptation robust to outliers and non‐visible surface regions and to guide the shape adaptation towards realistically appearing human shapes, we use a mesh‐Laplacian‐based shape prior. Both priors are learned/extracted from the training set of the template model learning phase. The output is a model adapted to the captured subject with respect to shape and kinematic skeleton as well as the animation parameters to resemble the observed movements. With example applications, we demonstrate the benefit of such footage. Experimental evaluations on publicly available datasets show the achieved natural appearance and accuracy.: In this paper, we address simultaneous markerless motion and shape capture from 3D input meshes of partial views onto a moving subject. We exploit a computer graphics model based on kinematic skinning as template tracking model. This template model consists of vertices, joints and skinning weights learned a priori from registered full‐body scans, representing true human shape and kinematics‐based shape deformations. Two data‐driven priors are used together with a set of constraints and cues for setting up sufficient correspondences. A Gaussian mixture model‐based pose prior of successive joint configurations is learned to soft‐constrain the attainable pose space to plausible human poses. To make the shape adaptation robust to outliers and non‐visible surface regions and to guide the shape adaptation towards realistically appearing human shapes, we use a mesh‐Laplacian‐based shape prior. Both priors are learned/extracted from the training set of the template model learning phase.