Inverse kinematics positioning using nonlinear programming for highly articulated figures
ACM Transactions on Graphics (TOG)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Hybrid Monte Carlo Filtering: Edge-Based People Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Physics-based Animation (Graphics Series)
Physics-based Animation (Graphics Series)
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Computer
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian-like spatial priors for articulated tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Automated gesture segmentation from dance sequences
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural metrics and least-committed priors for articulated tracking
Image and Vision Computing
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Statistical analysis of humans, their motion and their behaviour is a very well-studied problem. With the availability of accurate motion capture systems, it has become possible to use such analysis for animation, understanding, compression and tracking of human motion. At the core of the analysis lies a measure for determining the distance between two human poses; practically always, this measure is the Euclidean distance between joint angle vectors. Recent work [7] has shown that articulated tracking systems can be vastly improved by replacing the Euclidean distance in joint angle space with the geodesic distance in the space of joint positions. However, due to the focus on tracking, no algorithms have, so far, been presented for measuring these distances between human poses. In this paper, we present an algorithm for computing geodesics in the Riemannian space of joint positions, as well as a fast approximation that allows for large-scale analysis. In the experiments we show that this measure significantly outperforms the traditional measure in classification, clustering and dimensionality reduction tasks.