Precomputing Interactive Dynamic Deformable Scenes

1. Precomputing Interactive Dynamic Deformable Scenes Doug L.Jams and Kayvon Fatahailian 报告人：宋超

2. Physically Based Modeling and Interactive Simulation • Approach a. Analysis Method: to get analysis solution of the physics equation b. numerical method : FEA,caculus of differences,etc c. data driven ▪ Challenge: a. the difficulty of getting the analysis solution b. no-linear question widely exiting c. how to acquire the data? d. how to use the data?

3. About data-driven • An important strategy • How to identify and control complex systems • Former works ▪ Nelles 2000----Nural Network ▪ Reissell and Pai 2001 ----ARX models ▪ Atkeson et.al.1997---Locally weighted Learning (Lazy learningl) ▪ D.Jams and K. Fatahalian Impulse response functions (IRFs)

4. Precomputing Interactive Dynamic Deformable Scenes • Contribution ▪ black box offline simulators ▪ Dimensional Model Reduction • Excellence ▪ Robust ▪ Real-time ▪ Handle nonlinear deformation ▪ Illumination ▪ can be synthesized on programmable graphics hardware • Using Scope ▪ particular system ▪ very particular interaction conditions

5. Precomputing Interactive Dynamic Deformable Scenes • Procedure ▪ Fore treatment (including mesh ,creating the mechanics model) ▪ Dimensional Model Reduction ▪ Analyze the interaction condition ▪ Pre –calculate and create IBFs ▪ Implement

6. About Fore-treatment and acquire deterministic iteractor • Get geometric mesh • Determine the system DOF • Determine the pre-computing • According the interaction based on probability.

7. Dimensional Model Reduction(1) • Deterministic static space model Dynamics: Appearance • State nodes: • Time step edge: • Orbits: a temporal sequence of nodes,connected by time step edge • Discrete phase portrait(P): the collection of all pre-computed orbits

8. Dimensional Model Reduction(2) • Model Reduction Detail N state nodes,v vertices N displacement field that is (each u has 3 vector components)

9. Dimensional Model Reduction(3) • Model Reduction Detail ▲ a small number of vibration modes can be sufficient to approximate observed dynamics. (SVD) • Re-parameterization of the phase portrait the state vector:

10. Dimensional Model Reduction(3) • Reduced state vector coordination • ▲displacement • ▲velocity

11. Precomputation Process • Data-driven modeling complication insufficient data;high-demensional state space; divergence of nearby orbits;self-collisions.etc

12. Impulse Response Function • IRFs Index: • IRFs:

13. Impulse Response Functions(2) • An important special case :

14. Impulse Palettes • Impulse palette based on IRFs: • Impulsively sampling the phase portrait ▲sample time ▲no redundancy ▲orbits terminate

15. Simulate Implement • Blending Impulse Responses Approximate the IRF at That is

16. Example(1) • Dinosaur on moving car dashboard • Plant in moving pot • Cloth on moving door

17. Example(2) • The pre-computing time

18. Example

19. Example(3)

20. Thank you!