Titlebook: Human Motion - Understanding, Modeling, Capture and Animation; Second Workshop, Hum Ahmed Elgammal,Bodo Rosenhahn,Reinhard Klette Conferenc

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Boosted Multiple Deformable Trees for Parsing Human Posesthey fail to capture additional dependencies between body parts, other than kinematic constraints. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combi

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Multi-activity Tracking in LLE Body Pose Spacel embedding of the pose manifolds using Locally Linear Embedding (LLE), as well as the statistical relationship between body poses and their image appearance. In addition, the dynamics in these pose manifolds are modelled. Sparse kernel regressors capture the nonlinearities of these mappings efficie

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Exploiting Spatio-temporal Constraints for Robust 2D Pose Trackingels” is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the prob

老巫婆

Efficient Upper Body Pose Estimation from a Single Image or a Sequenceally manageable fitness function. This is accomplished by first parameterizing a tree structure by its joints. Candidate configurations can then efficiently and exhaustively be assembled in a bottom-up manner. Working from the leaves of the tree to its root, we maintain a list of locally optimal, ye

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Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing a tracked person. This is done relying on extracted silhouettes only. Thus, during the capture process the 3D model of a tracked person is learned..Depending on a sparse number of 2D-3D correspondences, that are computed along normal directions from image sequences of different cameras, a Laplacian

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