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Computer Generated Watercolor

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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. Background. NPR Purpose : aesthetic rather than technical Artificial art ?.

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Computer Generated Watercolor

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  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. Background • NPR • Purpose : aesthetic rather than technical • Artificial art ?

  3. Harold Cohen – 80’s

  4. Haeberli - 1990

  5. Meier - 1995

  6. Litwinowicz - 1997

  7. Hertzmann – 1998, 2001

  8. Gooch - 2001

  9. Today : Curtis et al. - 1997

  10. Overview • Particularities of Watercolor • Computer simulation • Fluid simulation • Kubelka-Munk rendering • Applications • Discussion

  11. Like no other medium • Beautiful textures and patterns • Reveals the motion of water • Luminous, glowing

  12. Blake (1757-1827)

  13. Turner (1775-1851)

  14. Constable (1776-1837)

  15. Cezanne (1839-1906)

  16. Kandinski (1866-1944)

  17. Klee (1879-1940)

  18. Carter (1955-)

  19. Watercolor materials • Paper • Pigments

  20. Dry brush Edge darkening Back runs Granulation Flow Glazing Watercolor effects

  21. Simulation..

  22. Fluid simulation I • 3 layers :

  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. Paper simulation • Supposedly : shape of every fiber matters • A simpler model : a height field • Generation : Perlin’s noise and Worley’s cellular textures

  25. Main loop • For each time step • Move Water • Update velocities • Relax Divergence • Flow Outward • Move Pigment • Transfer Pigment • Simulate Capillary Flow

  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. 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. 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. Optical compositing • Compute R and T : • Then compose : • Weight relatively to relative thicknesses

  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. Application I • Interactive painting :

  32. Application II • Watercolorization :

  33. Application III • 3D models :

  34. Future work • Other effects • Automatic rendering • Generalization • Animation

  35. Summary • A particular painting technique • A physically based simulation • Fluid motion • Optical compositing • Application and results

  36. Conclusion and discussion • Efficiency issues and long term interest • Border between art, physics and computer science

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