Composable controllers for physics-based character animation
Proceedings of the 28th 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
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Motion Perturbation Based on Simple Neuromotor Control Models
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Hybrid Control for Interactive Character Animation
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Dynamic response for motion capture animation
ACM SIGGRAPH 2005 Papers
Physically based grasping control from example
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Interaction capture and synthesis
ACM SIGGRAPH 2006 Papers
Dynamo: dynamic, data-driven character control with adjustable balance
Proceedings of the 2006 ACM SIGGRAPH symposium on Videogames
On the beat!: timing and tension for dynamic characters
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
SIMBICON: simple biped locomotion control
ACM SIGGRAPH 2007 papers
Simulating biped behaviors from human motion data
ACM SIGGRAPH 2007 papers
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
IEEE Computer Graphics and Applications
Performance capture with physical interaction
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review
Computer Graphics Forum
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Combining physically based simulation and motion capture data for animation is becoming a popular alternative to large motion databases for rich character motion. In this paper, our focus is on adapting motion-captured sequences for character response to external perturbations. Our technique is similar to approaches presented in the literature, but we propose a novel, straightforward way of computing feedforward control. While alternatives such as inverse dynamics and feedback error learning (FEL) exist, they are more complicated and require offline processing in contrast to our method which uses an auxiliary dynamic simulation to compute feedforward torques. Our method is simple, general, efficient, and can be performed at runtime. These claims are demonstrated through various experimental results of simulated impacts.