Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data
International Journal of Computer Vision
Flexible automatic motion blending with registration curves
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Temporal motion models for monocular and multiview 3D human body tracking
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Interactive Tracking of 2D Generic Objects with Spacetime Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Real-time pose estimation using constrained dynamics
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Model based full body human motion reconstruction from video data
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
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This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skeletal size from a small set of 2D image features tracked from uncalibrated monocular video sequences. The key idea of our approach is to construct a generative human motion model from a large set of preprocessed human motion examples to constrain the solution space of monocular human motion tracking. In addition, we learn a generative skeleton model from prerecorded human skeleton data to reduce ambiguity of the human skeleton reconstruction. We formulate the reconstruction process in a nonlinear optimization framework by continuously deforming the generative models to best match a small set of 2D image features tracked from a monocular video sequence. We evaluate the performance of our system by testing the algorithm on a variety of uncalibrated monocular video sequences.