1 / 36

Computer Generated Watercolor

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 ?.

cael
Download Presentation

Computer Generated Watercolor

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related