Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Modeling and Learning Contact Dynamics in Human Motion
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2008 classes
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
Unsupervised hierarchical modeling of locomotion styles
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers
International Journal of Computer Vision
Tracking human pose with multiple activity models
Pattern Recognition
A manifold representation as common basis for action production and recognition
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Animating non-humanoid characters with human motion data
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Manifold learning for object tracking with multiple motion dynamics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Predicting Articulated Human Motion from Spatial Processes
International Journal of Computer Vision
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
Probabilistic feature extraction from multivariate time series using spatio-temporal constraints
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Gaussian process motion graph models for smooth transitions among multiple actions
Computer Vision and Image Understanding
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Eyecatch: simulating visuomotor coordination for object interception
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
Two-layer dual gait generative models for human motion estimation from a single camera
Image and Vision Computing
Computer Vision and Image Understanding
Joint view-identity manifold for infrared target tracking and recognition
Computer Vision and Image Understanding
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In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean latent space. Our focus is the Gaussian Process latent variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable latent directions enabling, for example, style/content separation, and (b) generalise beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data.