Computer Generated Watercolor

Curtis, Anderson, Seims, Fleisher, Salesin

SIGGRAPH 1997

Presented byYann SEMET

Universite of Illinois at Urbana ChampaignUniversite de Technologie de Compiegne

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

Applications Discussion

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 brushb) Edge darkeningc) Back runs

d) Granulatione) Flowf) Glazing

Simulation..

Fluid simulation I 3 layers :

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.

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 Rw and Rb

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