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Efficient Precomputing Techniques for Interactive Dynamic Deformable Scenes

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biana
20 diapositives · 358 Views · Aug 23, 2014

This presentation explores advanced methods for precomputing interactive dynamic deformable scenes, focusing on a data-driven approach that addresses the challenges of nonlinear analysis solutions. We discuss various numerical methods, including Finite Element Analysis (FEA), and highlight key contributions such as black-box offline simulators and dimensional model reduction techniques. With an emphasis on robustness and real-time interaction, we aim to synthesize illumination effects on programmable graphics hardware while ensuring accurate modeling of complex systems. Case studies demonstrate practical applications in diverse scenarios.

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  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!