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Издательство Morgan Kaufmann, 2003, -806 pp.
What is a realistic image? This is an
age-old question in art, and a contemporary question in
computer graphics. This book provides a modern answer involving
the computer and a new definition of realism.
The classic definition of realism has been veridical realism.
Does the picture pass the comparison test? That is, would an
observer judge the picture to be real? This is traditionally
described by Pliny's story (in Book 35 of his Natural History)
of the ancient painter Zeuxis who painted a picture of a boy
carrying some grapes, and when the birds flew up to the
picture, he approached the work and, in irritation, said, ''I
have painted the grapes better than the boy, for if I had
rendered him perfectly, the birds would have been
afraid."
Nowadays the ultimate in fooling the eye is special effects in
the movies. Almost every movie involves hundreds of special
effects that are seamlessly combined with live action. It is
impossible to tell what is real and what is synthesized.
Equally amazing are full-length, computer-generated pictures
such as Shrek. Although few would be fooled into believing
these worlds are real, it is more the artistic choice of the
storyteller than a technological limitation. A major
achievement in the last two decades is that computers allowed
us to achieve veridical realism of imagined scenes.
Besides direct comparison, there are other definitions of real.
Masters such as Vermeer used optical devices to aid them in
painting realistic pictures, and modern photorealists such as
Richard Estes paint over a projected image of a photograph.
Thus, another definition of real is to be traced or copied from
an image. In this sense the montage of composite layers in a
movie is photoreal, since different elements come from
different film sequences. There are many other definitions of
realism. For example, real can mean a choice of subject matter,
such as everyday life versus a myth or an idealized form.
The definition of realism that I like the most is the one I
first heard from my colleague, then at Pixar, Alvy Ray Smith:
he claimed photorealism was roughly equivalent to visual
complexity. Two factors underlie visual complexity, diversity
in the types of primitives and their sheer numbers. This
definition resonates with computer scientists, since computers
are very good at both supporting a wide range of computational
primitives and processing enormous amounts of data. This book
is about using the computer to generate visual complexity, an
approach called procedural modeling.
The causes of visual complexity in the computer-generated image
are the ingredients of perception: color, texture, edges,
depth, and motion. The equivalents in object-space, or in the
scene, are color, pattern, reflection, illumination, shape, and
motion. All these factors come together in composite materials
such as wood, stone, and cloth and in natural phenomena such as
clouds, steam, smoke, fire, water, landscapes, and planetoids.
Procedural models for these myriad objects are the subjects of
this book.
Why are computers so good at generating visual complexity? The
reason is profound as well as practical.
First, computers expand the types of models that may be used.
For example, a surface may be defined as the zeros of an
implicit function of x, y, and z. The simplest implicit
functions are quadratic functions of the coordinates and define
the famous quadric surfaces: spheres, cones, cylinders, and so
on. Using a modern programming language with all its built-in
functions and arithmetic operators, much more complicated
expressions are just as easy to form and to evaluate. Perlin's
hypertextured surfaces arise from this flexibility and
generality.
Second, computers can generate many from few. A few parameters
(or a small amount of geometry) magically expand into a large,
detailed model. ''Data amplification" gives the user tremendous
power, leveraging their efforts and offloading tedious
specification of every single detail. A related concept is
Kolmogorov complexity, or the smallest program capable of
generating a given function. A very few lines of code can
produce beautiful pictures. The hacker "demoscene" dramatically
illustrates this idea. Here programmers are given the
constraint that the size of the file containing both code and
data (models, textures, sounds) must fit in less than 64KB.
From this file emerges a richly detailed animation.
Third, computational models are by necessity discrete and
finite. Although at first this may seem like a limitation,
since computational procedures must approximate continuous
mathematics and physics, it may in fact open up many new
possibilities. For example, an approximation of a smooth curve
may be generated from an n-sided polygon by a simple
corner-cutting algorithm. Each step consists of cutting off all
the corners of the polygon, replacing a vertex with an edge and
two new vertices. After an infinite number of iterations of the
cutting procedure, the input polygon will converge to a smooth
curve. However, on a computer, we can never perform an infinite
number of steps, so perfectly smooth curves can never be
constructed. Once we give up on idealized mathematical
smoothness, we can generalize corner-cutting polygons to
subdividing 3D polyhedral meshes; although these new algorithms
do not form smooth objects, a whole new universe of different
types of curves and surfaces can now be generated on the
computer.
For these reasons procedural modeling is a very powerful new
tool that is enabled by the computer. This approach is what is
fundamentally different about computer graphics and traditional
forms of image making.
An important issue that remains, the Achilles heel of this
approach, is controllability. Whether it is a physical
simulation with its initial or boundary conditions, or a
procedural model with its parameters, the end result must serve
the needs of the user. The benefit of filling in detail
automatically comes at a cost: the user loses control over the
details. The need for controllability drives the development of
interactive, what-you-see-is-what-you-get systems. This tension
between precise control and programmed complexity remains an
interesting research issue. In practice, virtual characters are
usually modeled manually, and their motion is generated using
keyframe animation. However, buildings, landscapes, and crowds
are increasingly being generated using procedural
techniques.
This new edition is particularly timely. Although the interest
in procedural modeling subsided for a while, there has suddenly
been an explosion of new research and development. Processing
power continues to increase faster than human modeling power,
and as a result models produced procedurally have a level of
detail that cannot be produced by hand. New approaches have
also emerged: machine learning has been coupled with procedural
modeling so that it is now possible to analyze and then
synthesize textures, shapes, motions, and styles from
examples.
Another major new development is programmable graphics
hardware. Graphics processing units, or GPUs, have always been
increasing in performance much faster than CPUs. In the last
few years, GPUs switched from a fixed-functionality to a
flexible, programmable graphics pipeline. Now it is possible to
download procedural models into these processors. Currently,
GPUs are mostly limited to evaluating procedural texture and
reflection models, but in the not too distant future they will
be able to produce geometry and motion procedurally as well.
Procedural models thus have technology on their side, since
they use less bandwidth and communication resources than
traditional approaches to graphics systems.
This book describes the complete toolbox of procedural
techniques from theory to practice. The authors are the key
inventors of the technology and some of the most creative
individuals I know. This book has always been my favorite
computer graphics book, and I hope you will enjoy it as much as
I have.
Building Procedural Textures
Real-Time Programmable Shading
Cellular Texturing
Advanced Antialiasing
Practical Methods for Texture Design
Procedural Modeling of Gases
Animating Solid Spaces
Volumetric Cloud Modeling with Implicit Functions
Issues and Strategies for Hardware Acceleration of Procedural
Techniques
Procedural Synthesis of Geometry
Noise, Hypertexture, Antialiasing, And Gesture
Real-Time Procedural Solid Texturing
A Brief Introduction to Fractals
Fractal Solid Textures: Some Examples
Procedural Fractal Terrains
QAEB Rendering for Procedural Models
Atmospheric Models
Genetic Textures
Mojoworld: Building Procedural Planets
On the Future: Engineering the Appearance of Cyberspace