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Computer Generated Watercolor

 Curtis, Anderson, Seims, Fleisher, Salesin
             SIGGRAPH 1997

                   ...
Background

   NPR
   Purpose : aesthetic rather than
    technical
   Artificial art ?
Harold Cohen – 80’s
Haeberli - 1990
Meier - 1995
Litwinowicz - 1997
Hertzmann – 1998, 2001
Gooch - 2001
Today : Curtis et al. - 1997
Overview
   Particularities of Watercolor
   Computer simulation
       Fluid simulation
       Kubelka-Munk rendering...
Like no other medium


   Beautiful textures and patterns
   Reveals the motion of water
   Luminous, glowing
Blake (1757-1827)
Turner (1775-1851)
Constable (1776-1837)
Cezanne (1839-1906)
Kandinski (1866-1944)
Klee (1879-1940)
Carter (1955-)
Watercolor materials


   Paper
   Pigments
Watercolor effects




a)   Dry brush        d)   Granulation
b)   Edge darkening   e)   Flow
c)   Back runs        f)   G...
Simulation..
Fluid simulation I
   3 layers :
Fluid simulation II
   Parameters of the simulation :
       Wet-area mask : M
       Velocities : u,v
       Pressure...
Paper simulation
   Supposedly : shape of every fiber
    matters
   A simpler model : a height field
   Generation : P...
Main loop
   For each time step
       Move Water
            Update velocities
            Relax Divergence
         ...
Conditions for realism
   Flow must be constrained so water
    remains within M
   Surplus of water causes flow outward...
Rendering : Kubelka-Munk
   For each pigment, 2 coeff. Per RGB layer :
       K : absorbtion
       S : scattering
   ...
Types of paints
   Opaque (e.g. Indian Red)
   Transparent (e.g. Quinacridone Rose)
   Interference (e.g. Interference ...
Optical compositing
   Compute R and T :

   Then compose :

   Weight relatively to relative thicknesses
Discussion of the KM model
   Assumptions partially satisfied :
       Identical refractive indices
       Random orien...
Application I
   Interactive painting :
Application II
   Watercolorization :
Application III
   3D models :
Future work

   Other effects
   Automatic rendering
   Generalization
   Animation
Summary

   A particular painting technique
   A physically based simulation
       Fluid motion
       Optical compos...
Conclusion and discussion


   Efficiency issues and long term interest
   Border between art, physics and
    computer ...
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Computer Generated Watercolor

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Computer Generated Watercolor

  1. 1. Computer Generated Watercolor Curtis, Anderson, Seims, Fleisher, Salesin SIGGRAPH 1997 Presented by Yann SEMET Universite of Illinois at Urbana Champaign Universite de Technologie de Compiegne
  2. 2. Background  NPR  Purpose : aesthetic rather than technical  Artificial art ?
  3. 3. Harold Cohen – 80’s
  4. 4. Haeberli - 1990
  5. 5. Meier - 1995
  6. 6. Litwinowicz - 1997
  7. 7. Hertzmann – 1998, 2001
  8. 8. Gooch - 2001
  9. 9. Today : Curtis et al. - 1997
  10. 10. Overview  Particularities of Watercolor  Computer simulation  Fluid simulation  Kubelka-Munk rendering  Applications  Discussion
  11. 11. Like no other medium  Beautiful textures and patterns  Reveals the motion of water  Luminous, glowing
  12. 12. Blake (1757-1827)
  13. 13. Turner (1775-1851)
  14. 14. Constable (1776-1837)
  15. 15. Cezanne (1839-1906)
  16. 16. Kandinski (1866-1944)
  17. 17. Klee (1879-1940)
  18. 18. Carter (1955-)
  19. 19. Watercolor materials  Paper  Pigments
  20. 20. Watercolor effects a) Dry brush d) Granulation b) Edge darkening e) Flow c) Back runs f) Glazing
  21. 21. Simulation..
  22. 22. Fluid simulation I  3 layers :
  23. 23. Fluid simulation II  Parameters of the simulation :  Wet-area mask : M  Velocities : u,v  Pressure : p  Concentration : gk  Height of paper : h  Physical properties : density, staining power, granularity, etc.  Fluid properties : saturation, capacity, etc.
  24. 24. Paper simulation  Supposedly : shape of every fiber matters  A simpler model : a height field  Generation : Perlin’s noise and Worley’s cellular textures
  25. 25. Main loop  For each time step  Move Water  Update velocities  Relax Divergence  Flow Outward  Move Pigment  Transfer Pigment  Simulate Capillary Flow
  26. 26. Conditions for realism  Flow must be constrained so water remains within M  Surplus of water causes flow outward  Flow must be damped to minimize oscillating waves  Flow is perturbed by texture of paper  Local changes have global effects  Outward flow to darken edges
  27. 27. Rendering : Kubelka-Munk  For each pigment, 2 coeff. Per RGB layer :  K : absorbtion  S : scattering  Supposedly : K and S are measured  Here : user provides Rw and Rb
  28. 28. Types of paints  Opaque (e.g. Indian Red)  Transparent (e.g. Quinacridone Rose)  Interference (e.g. Interference Lilac)  Different hues (e.g. Hansa Yellow)
  29. 29. Optical compositing  Compute R and T :  Then compose :  Weight relatively to relative thicknesses
  30. 30. Discussion of the KM model  Assumptions partially satisfied :  Identical refractive indices  Random orientation of pigments  Diffuse illumination  1 wavelength at a time  No chemical interaction  Works surprisingly well !  OK, because we’re looking for appearance, not actual modeling
  31. 31. Application I  Interactive painting :
  32. 32. Application II  Watercolorization :
  33. 33. Application III  3D models :
  34. 34. Future work  Other effects  Automatic rendering  Generalization  Animation
  35. 35. Summary  A particular painting technique  A physically based simulation  Fluid motion  Optical compositing  Application and results
  36. 36. Conclusion and discussion  Efficiency issues and long term interest  Border between art, physics and computer science

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