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 ?

Harold Cohen – 80’s

Haeberli

Meier

Litwinowicz

Hertzmann – 1998, 2001

Gooch

Today : Curtis et al

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

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

Blake ( )

Turner ( )

Constable ( )

Cezanne ( )

Kandinski ( )

Klee ( )

Carter (1955-)

Watercolor materials Paper Pigments

Watercolor effects a) Dry brush b) Edge darkening c) Back runs d) Granulation e) Flow f) Glazing

Simulation..

Fluid simulation I 3 layers :

Fluid simulation II Parameters of the simulation : Wet-area mask : M Velocities : u,v Pressure : p Concentration : g k Height of paper : h Physical properties : density, staining power, granularity, etc. Fluid properties : saturation, capacity, etc.

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

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

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

Rendering : Kubelka-Munk For each pigment, 2 coeff. Per RGB layer : K : absorbtion S : scattering Supposedly : K and S are measured Here : user provides R w and R b

Types of paints Opaque (e.g. Indian Red) Transparent (e.g. Quinacridone Rose) Interference (e.g. Interference Lilac) Different hues (e.g. Hansa Yellow)

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 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

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 compositing Application and results

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