A Computational Approach to Digital Chinese Painting and Calligraphy

by Xu, Songhua; Lau, Francis C. M.; Pan, Yunhe

• ISBN13: 9783540881476
• ISBN10: 3540881476
• Format: Hardcover
• Copyright: 2009-06-30
• Publisher: Springer-Verlag New York Inc

Summary

"A Computational Approach to Digital Chinese Painting and Calligraphy" is a technical book on computer science and its applications in the arts. It focuses on Oriental digital arts, in particular Chinese arts and painting, offering a multi-disciplinary treatment from the angles of computer graphics, interactive techniques, human-computer interaction, and artificial intelligence. The book also discusses the unique difficulties and challenges of using the computer to produce Oriental arts, including research results by the authors and their lessons and engineering experiences behind these efforts. Songhua Xu is a computer scientist of Zhejiang University and Yale University, as well as an honorary researcher of the University of Hong Kong. Francis C.M. Lau is Professor at the University of Hong Kong where he leads the Systems Research Group in the Department of Computer Science. Yunhe Pan is Professor of Computer Science at Zhejiang University as well as Deputy President of Chinese Academy of Engineering.

Author Biography

Songhua Xu is a computer scientist of Zhejiang University and Yale University, as well as an honorary researcher of the University of Hong Kong Francis C.M. Lau is Professor at the University of Hong Kong where he leads the Systems Research Group in the Department of Computer Science. Yunhe Pan is Professor of Computer Science at Zhejiang University as well as Deputy President of Chinese Academy of Engineering.

Table of Contents

Introduction
Computer Science and Fine Arts	p. 3
Why Use Computers for Arts?	p. 3
Computer as an Art Tool	p. 3
Computer as an Exceptional Art Tool	p. 5
Computers as Mind-talkers	p. 5
Digital Arts	p. 7
What Are Digital Arts?	p. 7
Manual or Automatic Art Creation	p. 7
Three Elements of Digital Arts	p. 8
Classification of the Book Chapters	p. 9
Examples of Digital Arts	p. 9
Digital Film	p. 10
Digital Painting	p. 10
Computer Music	p. 10
Digital Sculpture	p. 11
Computer Dance	p. 12
Computer Puppetry	p. 13
Computer Calligraphy	p. 14
Why Digital Arts Are Computationally Challenging?	p. 15
Lack of Semantic Understanding	p. 15
The Versatile Nature of Art	p. 15
Aesthetic Evaluation and Feedback	p. 16
Inhomogeneity between the Two Types of Intelligence	p. 16
References	p. 17
Computer Science in Painting: A Brief Survey
Computer Science in Paintings or Drawings	p. 23
Introduction	p. 23
Automatic Generation of Paintings and Drawings from Photographs	p. 23
Early Pioneering Work	p. 23
Representative Recent Work	p. 25
Generating Paintings via Human-computer Interaction	p. 29
Automatic Generation of Painterly Rendering Animation from Videos	p. 30
Interactive Generation of Painterly Rendering Images	p. 31
Automatic Generation of Painterly Rendering from 3D Models	p. 32
Automatic Generation of Illustrations and Line Drawings from 3D Models	p. 32
Generating Painterly Rendering Animations from 3D Models	p. 35
Domain Specific Special-purpose Painterly Rendition Generation	p. 36
Efficient Painterly Rendition Generation	p. 39
Special Support for Digital Painting	p. 40
Hardware Support for Digital Painting	p. 40
Multiresolutional Painting	p. 40
References	p. 42
Interactive Digital Painting and Calligraphy
Introduction to Interactive Digital Chinese Painting and Calligraphy	p. 51
Overview	p. 51
Background	p. 51
Previous Work	p. 52
Our Virtual Brush	p. 55
References	p. 56
Basic Algorithmic Framework of a Virtual Hairy Paintbrush System	p. 59
Overview	p. 59
Introduction	p. 59
Overview of E-brush and Related Research	p. 60
Our Work and Contributions	p. 62
Writing Primitives	p. 64
The Model and the States	p. 65
The Parametric Model of the Virtual Hairy Brush	p. 65
The Parametric Model of a Writing Primitive	p. 67
The Three States of a Brush	p. 70
Sampling of the Input Data	p. 72
Dynamic Adjustments of the Brush	p. 75
Estimating the Pysical Conditions of the Brush	p. 75
Dynamic Adjustment of the Middle Control Axis	p. 76
Dynamic Adjustment of the Middle Control Ellipse	p. 78
Dynamic Adjustment of the Tip Control Line	p. 79
Splitting of the Virtual Hairy Brush	p. 80
Ink Flowage between Writing Primitives	p. 80
The Writing Process	p. 81
Customizing the Brush	p. 83
Quality Parameters	p. 83
Configuring the Brush with Machine Intelligence	p. 84
System Implementation and Experiment Results	p. 87
Related Work	p. 89
DAB	p. 90
Virtual Brush by Wong and Ip	p. 93
Other Virtual Brush Models	p. 94
Conclusion and Future Work	p. 94
Summary and Conclusion	p. 94
Future Work	p. 95
References	p. 97
Performance Enhanced Virtual Hairy Paintbrush System	p. 103
Overview	p. 103
Introduction	p. 103
Modeling the Paintbrush's Geometry	p. 105
Three-layer Hierarchical Modeling	p. 106
Real-time Visual Display of the Brush	p. 108
Modeling the Paintbrush's Dynamic Behavior	p. 110
Deformation due to Brush-paper Collision	p. 112
Deformation due to Inner Stress	p. 114
Calibrating the On-line Results	p. 117
E-painting System based on Realistic Virtual Brush Modeling	p. 119
Additional Components of Our New Painting System	p. 119
The Running System	p. 121
Related Work	p. 121
Wong & Ip's System	p. 121
The DAB System	p. 123
Chu & Tai's System	p. 124
Conclusion and Future Work	p. 125
References	p. 126
Pigment Component of an Advanced Virtual Hairy Paintbrush System	p. 129
Overview	p. 129
Introduction	p. 129
Main Ideas	p. 130
Pigment Model and the Brush	p. 131
Organization of the Chapter	p. 132
Previous Work	p. 133
Pigment Behavior Models	p. 133
Comparison with Chu & Tai's Work	p. 134
Pigment Sorption between the Brush and the Paper Surface	p. 135
Pigment Diffusion on the Paper Surface	p. 137
Pigment Diffusion at the Brush Tip	p. 139
Evaporation	p. 141
At the Brush Tip Bundle	p. 141
On the Paper Surface	p. 141
Pigment Deposition on the Paper Fibers	p. 142
Rendering the Simulation Results	p. 143
Pigment Mixing with High Fidelity	p. 143
Superimposing the Layers	p. 147
Hardware-Accelerated Implementation	p. 148
Experiment Results	p. 148
Conclusion and Future Work	p. 153
References	p. 154
Rendering Component of an Advanced Virtual Hairy Paintbrush System	p. 159
Motivation	p. 159
Necessity and Importance of Brush Hair Rendering	p. 159
Performance Requirements	p. 160
Brush Hair versus Human Hair	p. 160
Overview	p. 161
Introduction	p. 161
Ideas and Contributions	p. 161
Organization of the Chapter	p. 162
Related Work	p. 162
Hair Rendering for Quality	p. 162
Hair Rendering for Speed	p. 163
Image-Based Rendering	p. 163
Appearance Modeling	p. 164
Hair Modeling and Representation	p. 165
Modeling Hair as Virtual Material	p. 165
Four-level Hierarchy of Hair Modeling	p. 165
Generalized Disk Structure for Representing Hair Clusters	p. 166
Hair Density Field for Sector	p. 168
HRIR-DB and Semantics-Aware Texture Function	p. 168
SATF and Our Offline/Online Two-phased Rendering Algorithm	p. 168
Minimizing the Size of HRIR-DB	p. 170
Constructing the Database of Hair Rendering Intermediate Results	p. 171
Deriving an HRIR Record	p. 171
Indexing an HRIR Record	p. 172
Fast and High Quality Online Hair Rendering	p. 173
Main Steps of Online Hair Rendering	p. 173
SATF and re- and alpha-map Construction	p. 174
Online Hair Lighting	p. 177
Online Hair Self-shadowing	p. 178
Deriving Shading through Integrating All the Rendering Effects Together	p. 179
Hardware Acceleration	p. 180
Experiment Results	p. 182
Conclusion and Future Work	p. 190
Conclusion	p. 190
Discussion and Future Work	p. 190
References	p. 195
Automatic Generation of Artistic Chinese Calligraphy
Principles of Automatic Generation of Artistic Chinese Calligraphy	p. 203
Overview	p. 203
Introduction	p. 203
Problem Formulation and Overall System Architecture	p. 206
Hierarchical and Parametric Representation	p. 208
Hierarchical Representation	p. 208
Six Levels of Parametric Representation	p. 208
Advantages of Our Representation	p. 210
Calligraphic Shape Decomposition	p. 210
Extracting Levels 0-1 Elements	p. 211
Extracting Levels 2-3 Elements	p. 211
Extracting Level 4 Elements	p. 212
Calligraphic Model Creation from Examples	p. 212
Principles of Calligraphic Model Creation	p. 212
Fusing Knowledge Sources in ARP	p. 213
A Computational Psychology Perspective	p. 214
Generating Artistic Calligraphy	p. 214
Extracting Aesthetic Constraints from Training Examples	p. 214
Past Results Reuse for Efficient Reasoning	p. 215
Experiment Results	p. 216
Possible Applications	p. 220
Conclusion and Future Work	p. 222
Conclusion	p. 222
Future Work	p. 222
References	p. 224
Two Perspectives on Automatic Generation of Artistic Chinese Calligraphy	p. 227
Overview	p. 227
A System Engineering Perspective on Automatic Generation of Artistic Chinese Calligraphy	p. 227
Hierarchical and Parametric Representation	p. 228
Hierarchical Representation	p. 228
Six Levels of Parametric Representation	p. 229
Deriving Parametric Representations for Constructive Elements	p. 230
Facsimiling Existent Calligraphy	p. 233
Extracting Levels 0-1 Elements	p. 233
Extracting Levels 2-3 Elements	p. 234
Extracting Level 4 Elements	p. 235
Generating New Calligraphy	p. 235
Principle of New Calligraphy Generation	p. 235
New Calligraphy Generation System	p. 236
Generating Artistic Calligraphy	p. 240
Constraints on the Process	p. 240
Extracting Aesthetic Constraints from Existent Artwork	p. 240
Constraint Satisfaction for Calligraphy Generation	p. 241
Relaxing the Aesthetic Constraints	p. 242
An Artificial Intelligence's Perspective on Automatic Generation of Artistic Chinese Calligraphy	p. 242
Background	p. 243
The Synthesis Reasoning Model	p. 243
Features of the Model	p. 244
Key Concepts of the Model	p. 244
The Computational Model of Synthesis Reasoning	p. 245
A Generic Methodology to Developing Synthesis Reasoning-based Intelligent Systems	p. 248
References	p. 249
A Preliminary Attempt at Evaluating the Beauty of Chinese Calligraphy	p. 253
Overview	p. 253
Introduction	p. 254
Motivation	p. 254
Chapter Organization	p. 255
Previous Work	p. 256
Calligraphy Representation	p. 257
Extracting Calligraphy Representation through a Two-phased Method	p. 258
Best-effort Automatic Stroke Extraction	p. 258
Intelligent User Interface for the Difficult Cases	p. 265
Calligraphy Aesthetics Evaluation	p. 267
Evaluating Shapes of Individual Strokes	p. 268
Evaluating Spatial Layout of Strokes	p. 271
Evaluating Coherence of Stroke Styles	p. 274
The Overall Evaluation	p. 275
Automatic Generation of Aesthetic Calligraphy	p. 276
Intelligent Calligraphy Tutoring System	p. 278
Conclusion and Future Work	p. 280
Conclusion	p. 280
Discussion and Future Work	p. 281
References	p. 285
Animating Chinese Paintings
Animating Chinese Paintings through Stroke-based Decomposition	p. 289
Overview	p. 289
Introduction	p. 289
Painting Decomposition Approach	p. 292
Image Segmentation	p. 295
Stroke Extraction by Region Merging	p. 295
Stroke Refinement and Appearance Capture	p. 301
Thin Brush Strokes	p. 302
Appearance Capture and Synthesis of Single Brush Strokes	p. 303
Single-stroke Appearance Model	p. 303
Why Direct Texture Mapping is Inadequate	p. 304
Separating Overlapping Brush Strokes	p. 306
Decomposition and Reconstruction Results	p. 309
Animating Paintings	p. 312
Discussion	p. 313
Conclusion and Future Work	p. 319
Conclusion	p. 319
Future Work	p. 319
References	p. 321
Perspectives
Final Fantasies for Digital Painting and Calligraphy	p. 327
Perspectives on Digital Paintbrush Research	p. 327
An Ideal Digital Paintbrush System	p. 327
A Surreal Digital Paintbrush System	p. 331
Perspectives on Intelligent Calligraphy Research	p. 340
An Ideal Intelligent Calligraphy System	p. 340
Intelligent Calligraphy System for Font Applications	p. 342
Intelligent Calligraphy Study for Other Applications	p. 345
An Ideal Painting Animation System	p. 347
References	p. 348
Index	p. 353
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