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Sig2011 seminar

Sig2011 seminar. Jin Zhou 2011.7.6. Animal Control Composite Control of Physically Simulated Characters (Tog 10) Character Animation in Two-Player Adversarial Games (Tog 10) Space-Time Planning with Parameterized Locomotion Controllers (Sig 11)

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Sig2011 seminar

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  1. Sig2011 seminar Jin Zhou 2011.7.6

  2. Animal Control • Composite Control of Physically Simulated Characters (Tog 10) • Character Animation in Two-Player Adversarial Games (Tog 10) • Space-Time Planning with Parameterized Locomotion Controllers (Sig 11) • Locomotion Skills for Simulated Quadrupeds (Sig 11) • Articulated Swimming Creatures (Sig 11) • Simulation • Efficient elasticity for character skinning with contact and collisions (Sig 11) • Sensitive Couture for Interactive Garment Design (Sig 11) • Physics-Inspired Upsampling for Cloth Simulation in Games

  3. Authors ? Uldarico Muico

  4. Motivation • Natural responses to certain disturbances • a single motion trajectory (traditional) • composite controllers that track multiple trajectories • Motion recovery

  5. Contributions • A trajectory tracking control framework • Learn a control policy over a rich set of motions • Combine these ideas cohesively in an interactive control setting

  6. Related work • State-based spring-damper systems • A broad set of skills • Hodgins et al. 1995; Hodgins and Pollard 1997;Wooten and Hodgins 2000; Faloutsos et al. 2001; Yin et al. 2007; Coros et al. 2008 • Reproduce motion trajectories within simulations • Laszlo et al. 1996; Zordan and Hodgins 2002; • Indistinguishable from motion capture

  7. Character model

  8. Control system • An example • a controller forwalking straight ahead • a controller for stepping sideways

  9. Create controller • System Dynamics • Trajectory tracking • Composite Trajectory Tracking • Constrained Control • Contact Adaptation

  10. Motion graph

  11. Slope tests Horizontal: slope angle Horizontal: slope angle Ordinate: walk speed Ordinate: walk speed (a): composited a faster motion (a): composited a faster motion (a): composited a faster motion (b): composited a slower motion (b): composited a slower motion (b): composited a slower motion (c): composited motions in (a) and (b) (c): composited motions in (a) and (b)

  12. Pushing tests Horizontal: force Ordinate: walk speed (a): composited a faster motion (b): composited a slower motion (c): composited motions in (a) and (b)

  13. Authors ? ? ? ? Kevin Wampler Erik Andersen Evan Herbst Yongjoon Lee Yongjoon Lee

  14. Motivation • Intelligent real-time controller • Game theory and long-term planning

  15. Related work • Character controller • Turn-based framework, e.g. tic-tac-toe • Randomized actions set, Lee and Lee [2006] • Reinforcement learning • generate intelligent single-character behavior • Ikemoto et al. [2005]

  16. Motion model • Parametric Motion Graph (PMG) [Heck and Gleicher 2007] • Allow simultaneous actions • Compact parameterized • Game state(node, time, parameters)

  17. Markov game • Stateless Games • E.g. rock-paper-scissors • Reward matrix • Policy vector • Linear Program

  18. Games With State

  19. Conclusion • Control characters • Game theory • Precompute a value function • A runtime controller

  20. Limitations • Cannot be applied to all possible games • two-player games

  21. Authors ? ? Sergey Levine Yongjoon Lee Vladlen Koltun

  22. Motivation • Characters traverse complex dynamic environments • A space-time planner

  23. Sample landmarks

  24. Locomotion controllers • Jumping • Obstacle avoidance • Corner

  25. Space-time planning

  26. Conclusion • The first of its kind for planning animations for virtual character • Ability of traversing complex, highly dynamic environments. • Generate high-quality animations from a large body of motion capture.

  27. Authors ? Stelian Coros Andrej Karpathy Benjamin Jones Lionel Reveret Michiel van de Panne

  28. Contributions • Several abstractions • a dual leg frame model • a flexible abstracted spine • the extensive use of internal virtual forces • A flexibly parameterized jump • Creation of gaits • walk, trot, pace, canter, and transverse and rotary gallop

  29. Quadruped model

  30. Controller overview

  31. Gaits • Gait graphs

  32. Virtual forces • Virtural forces controller

  33. Gait optimization • Motion capture data

  34. Limitations • Lack of self collisions • Motions of the head and tail are not modeled • A simple model of the feet

  35. Authors ? Jie Tan Yuting Gu Greg Turk C. Karen Liu

  36. Contribution • A general approach to creating realistic swimming behavior

  37. Related Work • Articulated Figure Control • Swimming trajectory • Optimization techniques • Solid-Fluid Coupling • Combine creature’s motions and fluid • Simulated Swimmers • Move the body parts of an articulated figure

  38. Articulated Rigid Body Simulation • Modified Proportional-Derivative Controller • a framework to compute control forces for tracking a kinematic state of a joint trajectory • Tan et al. [2011]

  39. Fluid Simulation • Use the inviscid, incompressible fluid equations • Coupling Articulated Figures with Fluids

  40. Path Following • turning maneuvers

  41. Limitations • soft-body creatures

  42. Author ? Aleka McAdams Yongning Zhu Andrew Selle Eftychios Sifakis

  43. Goal • Near-interactive simulation of skeleton driven • Soft tissue deformation for character animation

  44. Algorithm • Discretization

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