Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
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
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Kernel-Based Reinforcement Learning
Machine Learning
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Geostatistical motion interpolation
ACM SIGGRAPH 2005 Papers
Style translation for human motion
ACM SIGGRAPH 2005 Papers
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
Precomputing avatar behavior from human motion data
Graphical Models - Special issue on SCA 2004
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Motion synthesis and editing in low-dimensional spaces: Research Articles
Computer Animation and Virtual Worlds - CASA 2006
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Fat graphs: constructing an interactive character with continuous controls
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
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
Near-optimal character animation with continuous control
ACM SIGGRAPH 2007 papers
Constraint-based motion optimization using a statistical dynamic model
ACM SIGGRAPH 2007 papers
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Sparse multiscale gaussian process regression
Proceedings of the 25th international conference on Machine learning
Generalizing motion edits with Gaussian processes
ACM Transactions on Graphics (TOG)
Achieving good connectivity in motion graphs
Graphical Models
ACM SIGGRAPH Asia 2009 papers
Modeling spatial and temporal variation in motion data
ACM SIGGRAPH Asia 2009 papers
Real-time planning for parameterized human motion
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Interactive generation of human animation with deformable motion models
ACM Transactions on Graphics (TOG)
Motion fields for interactive character locomotion
ACM SIGGRAPH Asia 2010 papers
Physically valid statistical models for human motion generation
ACM Transactions on Graphics (TOG)
Proceedings of the ACM Symposium on Applied Perception
Sparse localized deformation components
ACM Transactions on Graphics (TOG)
Reconstructing whole-body motions with wrist trajectories
Graphical Models
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Interactive, task-guided character controllers must be agile and responsive to user input, while retaining the flexibility to be readily authored and modified by the designer. Central to a method's ease of use is its capacity to synthesize character motion for novel situations without requiring excessive data or programming effort. In this work, we present a technique that animates characters performing user-specified tasks by using a probabilistic motion model, which is trained on a small number of artist-provided animation clips. The method uses a low-dimensional space learned from the example motions to continuously control the character's pose to accomplish the desired task. By controlling the character through a reduced space, our method can discover new transitions, tractably precompute a control policy, and avoid low quality poses.