Interactive spacetime control for animation
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Hierarchical spacetime control
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Physically based motion transformation
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Motion capture-driven simulations that hit and react
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Efficient synthesis of physically valid human motion
ACM SIGGRAPH 2003 Papers
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Adaptation of performed ballistic motion
ACM Transactions on Graphics (TOG)
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Geostatistical motion interpolation
ACM SIGGRAPH 2005 Papers
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Constraint-based motion optimization using a statistical dynamic model
ACM SIGGRAPH 2007 papers
SIMBICON: simple biped locomotion control
ACM SIGGRAPH 2007 papers
Simulating biped behaviors from human motion data
ACM SIGGRAPH 2007 papers
Adaptive mixtures of local experts
Neural Computation
Interactive simulation of stylized human locomotion
ACM SIGGRAPH 2008 papers
Animating responsive characters with dynamic constraints in near-unactuated coordinates
ACM SIGGRAPH Asia 2008 papers
Generalizing motion edits with Gaussian processes
ACM Transactions on Graphics (TOG)
Theory of Applied Robotics: Kinematics, Dynamics, and Control
Theory of Applied Robotics: Kinematics, Dynamics, and Control
Contact-aware nonlinear control of dynamic characters
ACM SIGGRAPH 2009 papers
Modeling spatial and temporal variation in motion data
ACM SIGGRAPH Asia 2009 papers
Interactive generation of human animation with deformable motion models
ACM Transactions on Graphics (TOG)
Synthesis and editing of personalized stylistic human motion
Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games
VideoMocap: modeling physically realistic human motion from monocular video sequences
ACM SIGGRAPH 2010 papers
Intuitive Interactive Human-Character Posing with Millions of Example Poses
IEEE Computer Graphics and Applications
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Motion graphs++: a compact generative model for semantic motion analysis and synthesis
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Terrain runner: control, parameterization, composition, and planning for highly dynamic motions
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Guided latent space regression for human motion generation
Robotics and Autonomous Systems
Reconstructing whole-body motions with wrist trajectories
Graphical Models
Hi-index | 0.00 |
This article shows how statistical motion priors can be combined seamlessly with physical constraints for human motion modeling and generation. The key idea of the approach is to learn a nonlinear probabilistic force field function from prerecorded motion data with Gaussian processes and combine it with physical constraints in a probabilistic framework. In addition, we show how to effectively utilize the new model to generate a wide range of natural-looking motions that achieve the goals specified by users. Unlike previous statistical motion models, our model can generate physically realistic animations that react to external forces or changes in physical quantities of human bodies and interaction environments. We have evaluated the performance of our system by comparing against ground-truth motion data and alternative methods.