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
Expressive Gesture Animation Based on Non Parametric Learning of Sensory-Motor Models
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
The Visual Computer: International Journal of Computer Graphics - Special section on implicit surfaces
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
Interactive motion deformation with prioritized constraints
Graphical Models - Special issue on SCA 2004
Key-styling: learning motion style for real-time synthesis of 3D animation: Research Articles
Computer Animation and Virtual Worlds - CASA 2006
Learnt inverse kinematics for animation synthesis
Graphical Models - Special issue on the vision, video and graphics conference 2005
Constraint-based motion optimization using a statistical dynamic model
ACM SIGGRAPH 2007 papers
The Visual Computer: International Journal of Computer Graphics
What, why, where and how do children think? towards a dynamic model of spatial cognition as action
GW'11 Proceedings of the 9th international conference on Gesture and Sign Language in Human-Computer Interaction and Embodied Communication
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This paper presents a new method to generate arm gestures which reproduces the dynamical properties of human movements. We describe a model of synergy, defined as a coordinative structure responsible for the flexible organization of joints over time when performing a movement. We propose a generic method which incorporates this synergy model into a motion controller system based on any iterative inverse kinematics technique. We show that this method is independent of the task and can be parametrized to suit an individual using a novel learning algorithm based on a motion capture database. The method yields different models of synergies for reaching tasks that are confronted to the same set of example motions. The quantitative results obtained allow us to select the best model of synergies for reaching movements and prove that our method is independent of the inverse kinematic technique used for the motion controller.