Multivariate locally adaptive density estimation
Computational Statistics & Data Analysis
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
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
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
Adaptive mixtures of local experts
Neural Computation
Nonparametric density estimation for human pose tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
An Evolutionary Approach for Learning Motion Class Patterns
Proceedings of the 30th DAGM symposium on Pattern Recognition
Dealing with Self-occlusion in Region Based Motion Capture by Means of Internal Regions
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Action-specific motion prior for efficient Bayesian 3D human body tracking
Pattern Recognition
Efficient and robust annotation of motion capture data
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Learning local models for 2D human motion tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning to Recognize Objects in Images Using Anisotropic Nonparametric Kernels
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
A robust approach to multi-feature based mesh segmentation using adaptive density estimation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution and let the data speak for themselves. In general, kernel density estimators suffer from the problem known as the curse of dimensionality, i.e., the amount of data required to cover the whole input space grows exponentially with the dimension of this space. In many applications, such as human motion tracking, though, this problem turns out to be less severe, since the relevant data concentrate in a much smaller subspace than the original high-dimensional space. As we demonstrate in this paper, the concentration of human motion data on lower-dimensional manifolds, approves kernel density estimation as a transparent tool that is able to model priors on arbitrary mixtures of human motions. Further, we propose to support the ability of kernel estimators to capture distributions on low-dimensional manifolds by replacing the standard isotropic kernel by an adaptive, anisotropic one.