Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Practical parameterization of rotations using the exponential map
Journal of Graphics Tools
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Temporal motion models for monocular and multiview 3D human body tracking
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Tracking 3D Human Motion in Compact Base Space
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Multifactor Gaussian process models for style-content separation
Proceedings of the 24th international conference on Machine learning
Responsive characters from motion fragments
ACM SIGGRAPH 2007 papers
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulated mesh animation from multi-view silhouettes
ACM SIGGRAPH 2008 papers
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Achieving good connectivity in motion graphs
Graphical Models
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
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
Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
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We propose a unified model for human motion prior with multiple actions. Our model is generated from sample pose sequences of the multiple actions, each of which is recorded from real human motion. The sample sequences are connected to each other by synthesizing a variety of possible transitions among the different actions. For kinematically-realistic transitions, our model integrates nonlinear probabilistic latent modeling of the samples and interpolation-based synthesis of the transition paths. While naive interpolation makes unexpected poses, our model rejects them (1) by searching for smooth and short transition paths by employing the good properties of the observation and latent spaces and (2) by avoiding using samples that unexpectedly synthesize the nonsmooth interpolation. The effectiveness of the model is demonstrated with real data and its application to human pose tracking.