Fourier principles for emotion-based human figure animation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Morphable Models for the Analysis and Synthesis of Complex Motion Patterns
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Model-Based Estimation of 3D Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Flexible automatic motion blending with registration curves
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Problems, ongoing research and future directions in motion research
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
Human motion analysis for biomechanics and biomedicine
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to track 3D human motion from silhouettes
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Trajectory synthesis by hierarchical spatio-temporal correspondence: comparison of different methods
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Learning silhouette features for control of human motion
ACM Transactions on Graphics (TOG)
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Reconstruction of Periodic Motion from a Single View
International Journal of Computer Vision
Reconstructing and analyzing periodic human motion from stationary monocular views
Computer Vision and Image Understanding
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We present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Using Fourier decomposition, we construct a compact representation for periodic human motion. A low-dimensional linear motion model is learned from a training set of 3D Fourier representations by means of principal components analysis. Two-dimensional test data are projected onto this model with two approaches: least-square minimization and calculation of a maximum a posteriori probability using the Bayes' rule. We present two different experiments in which both approaches are applied to 2D data obtained from 3D walking sequences projected onto a plane. In the first experiment, we assume the viewpoint is known. In the second experiment, the horizontal viewpoint is unknown and is recovered from the 2D motion data. The results demonstrate that by using the linear model, not only can missing motion data be reconstructed, but unknown view angles for 2D test data can also be retrieved.