Motion Libraries for Character Animation

Our goal is to enable non-skilled computer users to use computer animation as an expressive medium. The fundamental paradigm behind our approach involves the creation of motion libraries which can be easily mapped onto different characters, or modified to fit the needs of a specific animation. We model the motion as an optimal dynamic process which allows us to preserve dynamic properties of the animation during editing. This formulation also enables us to intuitively edit high-level motion concepts such as the time and placement of footprints, length and mass of various extremities or joint arrangement

Published Papers

Synthesis of Complex Dynamic Character Motion from Simple Animations

We present a general method for rapid prototyping of realistic character motion. Our framework can be used to produce relatively complex realistic motion with little user effort.

Style-based Inverse Kinematics

We represent an inverse kinematics system based on learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraint, in real-time. Training the model on different input data leads to different styles of IK.

Momentum-based Parameterization of Dynamic Character Motion

This paper presents a system for rapid editing of highly dynamic motion capture data. The user can efficiently generate a large family of realistic motions from a single input motion, populating the dynamic space of motions by simple interpolation.

Learning Physics-Based Motion Style with Nonlinear Inverse Optimization

This paper presents a novel physics-based representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task.

Composition of Complex Optimal Multi-Character Motions

This paper presents a framework for extending space-time optimizations to significantly more complex motions of multiple characters over longer time periods.

		Synthesis of Complex Dynamic Character Motion from Simple Animations We present a general method for rapid prototyping of realistic character motion. Our framework can be used to produce relatively complex realistic motion with little user effort.
		Style-based Inverse Kinematics We represent an inverse kinematics system based on learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraint, in real-time. Training the model on different input data leads to different styles of IK.
		Momentum-based Parameterization of Dynamic Character Motion This paper presents a system for rapid editing of highly dynamic motion capture data. The user can efficiently generate a large family of realistic motions from a single input motion, populating the dynamic space of motions by simple interpolation.
		Learning Physics-Based Motion Style with Nonlinear Inverse Optimization This paper presents a novel physics-based representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task.
		Composition of Complex Optimal Multi-Character Motions This paper presents a framework for extending space-time optimizations to significantly more complex motions of multiple characters over longer time periods.