Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Tracking articulated objects by learning intrinsic structure of motion
Pattern Recognition Letters
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Action recognition feedback-based framework for human pose reconstruction from monocular images
Pattern Recognition Letters
Three Dimensional Monocular Human Motion Analysis in End-Effector Space
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Modeling spatial and temporal variation in motion data
ACM SIGGRAPH Asia 2009 papers
Indexing 3-D human motion repositories for content-based retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Generating Video Textures by PPCA and Gaussian Process Dynamical Model
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers
International Journal of Computer Vision
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
Bayesian reinforcement learning in continuous pomdps with Gaussian processes
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning local models for 2D human motion tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Motion fields for interactive character locomotion
ACM SIGGRAPH Asia 2010 papers
Multiple-activity human body tracking in unconstrained environments
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Self-occlusion handling for human body motion tracking from 3D ToF image sequence
Proceedings of the 1st international workshop on 3D video processing
Estimation system for forces and torques in a biped motion
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Gaussian-like spatial priors for articulated tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Stick it articulated tracking using spatial rigid object priors
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Transfer latent variable model based on divergence analysis
Pattern Recognition
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
Data-driven importance distributions for articulated tracking
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language
Pattern Recognition Letters
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Real-time stylistic prediction for whole-body human motions
Neural Networks
Target tracking without line of sight using range from radio
Autonomous Robots
Statistical gesture models for 3d motion capture from a library of gestures with variants
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Gaussian process motion graph models for smooth transitions among multiple actions
Computer Vision and Image Understanding
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Natural metrics and least-committed priors for articulated tracking
Image and Vision Computing
Proceedings of the ACM Symposium on Applied Perception
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
Spatial measures between human poses for classification and understanding
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Multi-view body tracking with a detector-driven hierarchical particle filter
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Human context: modeling human-human interactions for monocular 3d pose estimation
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Toward a unified framework of motion understanding
Image and Vision Computing
Engineering Applications of Artificial Intelligence
Spatio-temporal LTSA and its application to motion decomposition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
3D motion estimation of human body from video with dynamic camera work
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
Information capacity of full-body movements
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tracking in object action space
Computer Vision and Image Understanding
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
Proceedings of the ACM Symposium on Applied Perception
Gaussian Process Gauss-Newton for non-parametric simultaneous localization and mapping
International Journal of Robotics Research
Non-parametric hand pose estimation with object context
Image and Vision Computing
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
Nonparametric guidance of autoencoder representations using label information
The Journal of Machine Learning Research
Discriminative fusion of shape and appearance features for human pose estimation
Pattern Recognition
Motion planning and reactive control on learnt skill manifolds
International Journal of Robotics Research
Reconstructing whole-body motions with wrist trajectories
Graphical Models
Mixtures of Gaussian process models for human pose estimation
Image and Vision Computing
Joint view-identity manifold for infrared target tracking and recognition
Computer Vision and Image Understanding
Training energy-based models for time-series imputation
The Journal of Machine Learning Research
Efficient tracking using a robust motion estimation technique
Multimedia Tools and Applications
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We introduce Gaussian process dynamical models (GPDM) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensionalmotion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian process priors for both the dynamics and the observation mappings. This results in a non-parametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach, and compare four learning algorithms on human motion capture data in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.