Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Variational Approach to Recovering a Manifold from Sample Points
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Monocular Tracking of the Human Arm in 3D: Real-Time Implementation and Experiments
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Human 3D motion computation from a varying number of cameras
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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One of the main difficulties when reconstructing human motion from monocular video is the depth ambiguity. Achieving a reconstruction, given the projection of the joints, can be regarded as a search-problem, where the objective is to find the most likely configuration. One inherent problem in such a formulation is the definition of "most likely". In this work, we will pick the configuration that best complies with a set of training-data in a qualitative sense. The reason for doing this is to allow for large individual variation within the class of motions, and avoid an extreme bias towards the training-data. In order to capture the qualitative constraints, we have used a set of 3D motion capture data of walking people. The method is tested on orthographic projections of motion capture data, in order to compare the achieved reconstruction with the original motion.