Dynamo: dynamic, data-driven character control with adjustable balance

Dynamo (DYNAmic MOtion capture) is an approach to controlling animated characters in a dynamic virtual world. Leveraging existing methods, characters are simultaneously physically simulated and driven to perform kinematic motion (from mocap or other sources). Continuous simulation allows characters to interact more realistically than methods that alternate between ragdoll simulation and pure motion capture.The novel contributions of Dynamo are world-space torques for increased stability and a weak root spring for plausible balance. Promoting joint target angles from the traditional parent-bone reference frame to the world-space reference frame allows a character to set and maintain poses robust to dynamic interactions. It also produces physically plausible transitions between motions without explicit blending. These properties are maintained over a wide range of servo gain constants, making Dynamo significantly easier to tune than parent-space control systems. The weak root spring tempers our world-space model to account for external constraints that should break balance. This root spring provides an adjustable parameter that allows characters to fall when significantly unbalanced or struck with extreme force.We demonstrate Dynamo through in-game simulations of characters walking, running, jumping, and fighting on uneven terrain while experiencing dynamic external forces. We show that an implementation using standard physics (ODE) and graphics (G3D/OpenGL) engines can drive game-like applications with hundreds of rigid bodies and tens of characters, using about 0.002s of CPU time per frame.

[1]  Ken Shoemake,et al.  Animating rotation with quaternion curves , 1985, SIGGRAPH.

[2]  David C. Brogan,et al.  Animating human athletics , 1995, SIGGRAPH.

[3]  Jessica K. Hodgins,et al.  Adapting simulated behaviors for new characters , 1997, SIGGRAPH.

[4]  Michael Gleicher,et al.  Retargetting motion to new characters , 1998, SIGGRAPH.

[5]  Michael F. Cohen,et al.  Verbs and Adverbs: Multidimensional Motion Interpolation , 1998, IEEE Computer Graphics and Applications.

[6]  Zoran Popovic,et al.  Physically based motion transformation , 1999, SIGGRAPH.

[7]  John P. Lewis,et al.  Pose Space Deformation: A Unified Approach to Shape Interpolation and Skeleton-Driven Deformation , 2000, SIGGRAPH.

[8]  Jessica K. Hodgins,et al.  Simulating leaping, tumbling, landing and balancing humans , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Petros Faloutsos,et al.  Composable controllers for physics-based character animation , 2001, SIGGRAPH.

[10]  Jessica K. Hodgins,et al.  Motion capture-driven simulations that hit and react , 2002, SCA '02.

[11]  Lucas Kovar,et al.  Motion Graphs , 2002, ACM Trans. Graph..

[12]  David A. Forsyth,et al.  Motion synthesis from annotations , 2003, ACM Trans. Graph..

[13]  Frédéric H. Pighin,et al.  Hybrid control for interactive character animation , 2003, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings..

[14]  Lucas Kovar,et al.  Flexible automatic motion blending with registration curves , 2003, SCA '03.

[15]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[16]  Maja J. Mataric,et al.  Performance-Derived Behavior Vocabularies: Data-Driven Acquisition of Skills from Motion , 2004, Int. J. Humanoid Robotics.

[17]  Lucas Kovar,et al.  Automated extraction and parameterization of motions in large data sets , 2004, ACM Trans. Graph..

[18]  Taku Komura,et al.  Animating reactive motions for biped locomotion , 2004, VRST '04.

[19]  Martin A. Giese,et al.  On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics , 2003, Int. J. Humanoid Robotics.

[20]  Jovan Popović,et al.  Style translation for human motion , 2005, ACM Trans. Graph..

[21]  Jessica K. Hodgins,et al.  Analyzing the physical correctness of interpolated human motion , 2005, SCA '05.

[22]  C. Karen Liu,et al.  Learning physics-based motion style with nonlinear inverse optimization , 2005, ACM Trans. Graph..

[23]  Victor B. Zordan,et al.  Dynamic response for motion capture animation , 2005, SIGGRAPH '05.