Transcript CGWatercolor

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 - 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)
b)
c)
Dry brush
Edge darkening
Back runs
d)
e)
f)
Granulation
Flow
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