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Image-Guided Weathering: A New Approach for Flow Phenomena

This research paper introduces a novel method for simulating aging and weathering effects in computer graphics using image guidance. The approach combines physically-based simulation with data-driven methods to achieve more realistic visual detail. The paper focuses specifically on flow stains as a representative case and presents the process of extracting data from exemplars, simulating new effects on scenes, and improving parameter fitting. The results demonstrate the effectiveness of the proposed method in achieving natural variations and filling the gap between data-driven and simulation techniques.

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Image-Guided Weathering: A New Approach for Flow Phenomena

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  1. Image-Guided Weathering:A New Approach Applied to Flow Phenomena C. Bosch1, P. Y. Laffont, H. Rushmeier, J. Dorsey, G. Drettakis Yale University – REVES/INRIA Sophia Antipolis 1 Currently at ViRVIG, University of Girona

  2. Aging and Weathering • Essential for modeling urban environments • Governed by physical, chemical and biological processes

  3. Flow effects • Particularly complex • Flow over the scene (global effect) • Material properties (local effect)

  4. Aging and Weathering in CG • Physically-based simulation • Difficult to get the desired effect • Texture synthesis • Restricted by input information • Global effects particularly hard

  5. Motivation • Physically-based simulation • More flexible, allows global effects • Two main difficulties • Choosing appropriate parameters to achieve a given effect • Obtaining realistic visual detail

  6. Image-Guided Weathering • Use images to guide simulation • Flow stains as a representative case Exemplar New simulation

  7. Overview (I) • Extract data from exemplars • Color information • Simulation parameters • High frequency details Si= 1.301 rt = 0.252 kS = 0.0201 at = 0.404 kD = 0.0807 T = 803 ka,t = 0.021 Exemplar Data

  8. Overview (II) • Simulate new effects on scenes Si= 1.301 rt = 0.252 kS = 0.0201 at = 0.404 kD = 0.0807 T = 803 ka,t = 0.021 Data

  9. Related Work • Simulation • Phenomenon-specific [Merillou08] • Flow stains [Dorsey96; Chen05; Endo10] • Capture-and-transfer (synthesis) • Single image [Wang06; Xue08] • Acquisition systems [Gu06; Mertens06; Sun07; Lu07] • Inverse procedural textures [Bourque04; Lefebvre00]

  10. Flow model • Particle-based simulation [Dorsey96] • Absorption, solubility and deposition • Stain concentration maps • Parameters • Particles: mass (m), Si • Stain material: kS, kD • Target materials: a, ka, roughness (r) • Simulation: time (t), particle rate (N)

  11. Extracting Stains • Based on Appearance Manifolds [Wang06] Appearance Manifold Exemplar Degree Map

  12. Simulation Error Parameter Fitting Proxy geometry • Degree map = Stain concentration map Input stain Degree map source target Initial parameters Si= 1 kS = 0.04 kD= 0.04 rt= 0.2 at= 0.3 ka,t = 0.05 T = 300 image plane Si= 1.3 kS = 0.02 kD= 0.08 rt= 0.25 at= 0.4 ka,t = 0.02 T = 803 Error < threshold or max. iterations (Levenberg-Marquardt) [Lourakis04] Stop New parameters

  13. Improving Fitting • Stain distribution along the source • Accumulate degree from bottom to top

  14. Improving Fitting (II) • Flow deflection along the target • Compute local degree distribution (~vector field)

  15. Simulation Error Parameter Fitting (II) Proxy geometry Stain distribution Input stain Degree map source target Initial parameters Vector field Si= 1 kS = 0.04 kD= 0.04 rt= 0.2 at= 0.3 ka,t = 0.05 T = 300 image plane Si= 1.3 kS = 0.02 kD= 0.08 rt= 0.25 at= 0.4 ka,t = 0.02 T = 803 Error < threshold or max. iterations (Levenberg-Marquardt) [Lourakis04] Stop New parameters

  16. Fitting Results (w/o vector field) Using source distribution Exemplar Degree Map Simulation

  17. Fitting Results (w/o vector field) Exemplar Degree Map Simulation

  18. Fitting Results (w/ vector field) Exemplar w/o vfield Degree Map Simulation

  19. Fitting Results (w/ vector field) Exemplar Degree Map Simulation

  20. Fitting Results (w/ vector field) Exemplar Degree Map Simulation

  21. Fitting Results (Complex Targets) Exemplar Degree Map Simulation

  22. Stain Detail • Simulation lacks spatial variations (high-frequency detail) Degree Map Simulation Exemplar

  23. Detail Maps • Extract detail by image difference • Use guided texture synthesis [Lefebvre05] • Detail maps will modify stain adhesion Detail Map Degree Map Simulation Difference

  24. Simulating New Stains • Link data to stain sources and targets • Parameters, detail maps, color • Use 1D texture synthesis for distributions • Run flow simulation • Flow deflected by target geometry (+ disp. map)

  25. Color Transfer target background • Transfer stain color from input image • Background mixed with stain everywhere • Non-linear relationship between color and degree • Use per-pixel warping background color fully stained

  26. Results

  27. Results (II)

  28. Results (III)

  29. Results (IV)

  30. Performance • Preprocessing • Degree map: 1-3 minutes • Fitting: 30-60 minutes (500 iter., ~256x512) • Detail synthesis: 1-2 minutes (1024x1024) • Final simulation • Stain simulation: 2-5 minutes/stain • Color warping: 5-8 seconds/stain (1024x1024)

  31. Limitations • Good extraction from background • Fitting: Not true physical estimations • Detail maps: Depend on appropriate fit • Computation time

  32. Conclusions • New approach to acquire simulation data from photographs • Solves parameter estimation from images • Combines simulation with data-driven methods • Appearance manifold, texture synthesis, … • Fills the gap between data-driven and simulation Easy to use Natural variations (including global effects)

  33. Future work • Extend to other weathering phenomena • Deal with large scale scenes • Fast simulation, global effects, …

  34. Acknowledgements • Visiting grant U.Girona • ANR project (ANR-06-MDCA-004-01) • ERCIM “Alain Bensoussan” Fellowship • Autodesk (Maya/MentalRay) • Coding help: Li-Ying, Su Xue • Scene treatment: S. Close and F. Andrade-Cabral

  35. Thank you

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