Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Full Body Tracking from Multiple Views Using Stochastic Sampling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Regression-based Hand Pose Estimation from Multiple Cameras
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
Ambiguity Modeling in Latent Spaces
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Monocular 3D tracking of articulated human motion in silhouette and pose manifolds
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Learning grasping points with shape context
Robotics and Autonomous Systems
Dual gait generative models for human motion estimation from a single camera
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Animating non-humanoid characters with human motion data
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Latent gaussian mixture regression for human pose estimation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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
Facial movement based recognition
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Sparse structured probabilistic projections for factorized latent spaces
Proceedings of the 20th ACM international conference on Information and knowledge management
Gaussian process motion graph models for smooth transitions among multiple actions
Computer Vision and Image Understanding
Discriminative human full-body pose estimation from wearable inertial sensor data
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
No bias left behind: covariate shift adaptation for discriminative 3d pose estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
Non-parametric hand pose estimation with object context
Image and Vision Computing
Mixtures of Gaussian process models for human pose estimation
Image and Vision Computing
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We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM) [1] encapsulating both pose and silhouette features Our method is generative, this allows us to model the ambiguities of a silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.