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

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书目名称Human Motion - Understanding, Modeling, Capture and Animation
副标题Second Workshop, Hum
编辑Ahmed Elgammal,Bodo Rosenhahn,Reinhard Klette
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Human Motion - Understanding, Modeling, Capture and Animation; Second Workshop, Hum Ahmed Elgammal,Bodo Rosenhahn,Reinhard Klette Conferenc
描述This LNCS volume contains the papers presented at the second Workshop on Human Motion Understanding, Modeling, Capture and Animation, which took place on October 20th, 2007, accompanying the 11th IEEE International C- ference on Computer Vision in Rio de Janeiro, Brazil. In total, 38 papers were submitted to this workshop,of which 22 papers were accepted. We were careful to ensure a high standard of quality when selecting the papers. All submissions were double-blind reviewed by at least two experts. Out of the 22 accepted papers, 10 were selected for oral presentation and 12 for posters. We thank the authors of the accepted papers for taking the reviewers’ comments into account in the ?nal published versions of their papers. We thank all of the authors who submitted their work, and we trust that the reviewers’ comments have been of value for their research activities. The accepted papers re?ect the state of the art in the ?eld and cover various topicsrelatedto humanmotiontrackingandanalysis.Thepapersinthisvolume have been classi?ed into three categories based on the topics they cover: human motion capture and pose estimation, body and limb tracking and segmentation, and activity r
出版日期Conference proceedings 2007
关键词3D; Animation; ICCV; Moment; Stereo; anatomically correct modeling; cognition; deformable modeling; human mo
版次1
doihttps://doi.org/10.1007/978-3-540-75703-0
isbn_softcover978-3-540-75702-3
isbn_ebook978-3-540-75703-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
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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
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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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