Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Human Motion Signatures: Analysis, Synthesis, Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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
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
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
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Impact of Dynamics on Subspace Embedding and Tracking of Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Multifactor Gaussian process models for style-content separation
Proceedings of the 24th international conference on Machine learning
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Learned, activity-specific motion models are useful for human pose and motion estimation. Nevertheless, while the use of activity-specific models simplifies monocular tracking, it leaves open the larger issues of how one learns models for multiple activities or stylistic variations, and how such models can be combined with natural transitions between activities. This paper extends the Gaussian process latent variable model (GP-LVM) to address some of these issues. We introduce a new approach to constraining the latent space that we refer to as the locally-linear Gaussian process latent variable model (LL-GPLVM). The LL-GPLVM allows for an explicit prior over the latent configurations that aims to preserve local topological structure in the training data. We reduce the computational complexity of the GPLVM by adapting sparse Gaussian process regression methods to the GP-LVM. By incorporating sparsification, dynamics and back-constraints within the LL-GPLVM we develop a general framework for learning smooth latent models of different activities within a shared latent space, allowing the learning of specific topologies and transitions between different activities.