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Fast Texture Synthesis using Tree-structured Vector Quantization. Li-Yi Wei Marc Levoy. Computer Graphics Group Stanford University. Introduction. Texture Synthesis. Input. Result. Desirable Properties. Result looks like the input Efficient General Easy to use - PowerPoint PPT Presentation
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Fast Texture Synthesis using Fast Texture Synthesis using Tree-structured Vector Tree-structured Vector
QuantizationQuantization
Li-Yi Wei Marc Levoy
Computer Graphics GroupStanford University
IntroductionIntroduction
Texture Synthesis
Input
Result
Desirable PropertiesDesirable Properties
• Result looks like the input
• Efficient
• General
• Easy to use
• Extensible
Previous WorkPrevious Work
• Procedural Synthesis– Perlin 85, Witkin 91, Worley 96
• Statistical Feature Matching– Heeger 95, De Bonet 97, Simoncelli 98
• Markov Random Fields– Popat 93, Efros 99
OutlineOutline
• Basic algorithm
• Multi-resolution algorithm
• Acceleration
• Applications
Texture ModelTexture Model
• Textures are– local– stationary
• Model textures by – local spatial neighborhoods
Basic AlgorithmBasic Algorithm
• Exhaustively search neighborhoods
NeighborhoodNeighborhood
• Use causal neighborhoods
Causal Non-causal
Input
Noise
NeighborhoodNeighborhood
• Neighborhood size determines the quality & cost
33 55 77
99 1111 4141
423 s 528 s 739 s
1020 s 1445 s 24350 s
Multi-resolution PyramidMulti-resolution Pyramid
High resolution Low resolution
Multi-resolution Multi-resolution AlgorithmAlgorithm
BenefitBenefit
• Better image quality & faster computation
1 level55
3 levels55
1 level1111
ResultsResults
Random Oriented
Regular Semi-regular
FailuresFailures
• Non-planar structures
• Global information
ComparisonComparison
Heeger 95 De Bonet 97 Efros 99 Our method
Input
1941 secs 503 secs
12 secs
Acceleration Acceleration
• Computation bottleneck: neighborhood search
Nearest Point SearchNearest Point Search
• Treat neighborhoods as high dimensional points
1 2 3 4 5
6 7 8 9 10
11 12
Neighborhood
1 2 3 4 5 6 7 8 9 10 11 12
High dimensional point/vector
AccelerationAcceleration
• Nearest point search in high dimensions– [Nene 97]
• Cluster-based model for textures– [Popat 93]
• Tree-structured Vector Quantization– [Gersho 92]
Tree-structured Tree-structured Vector QuantizationVector Quantization
TimingTiming
• Time complexity : O(log N) instead of O(N)– 2 orders of magnitude speedup for non-trivial images
1941 secs 503 secs 12 secs
Efros 99 Full searching TSVQ
Results: Results: Brodatz TexturesBrodatz Textures
Input Exhaustive: 360 secs TSVQ: 7.5 secs
D103
D20
Application 1: Application 1: Constrained SynthesisConstrained Synthesis
?
Possible SolutionPossible Solution
• Multi-resolution blending [Burt & Adelson 83]– produce visible boundaries
Possible SolutionPossible Solution
• Original raster-scan algorithm– discontinuities at right and bottom boundaries
Possible SolutionPossible Solution
• Adaptive neighborhoods [Efros 99]– Hard to accelerate
Modifications Modifications
• Need to use a single symmetric neighborhood
• 2 pass algorithm with extrapolation
• Spiral order synthesis
ResultResult
ResultResult
• Extrapolation
??
??
ResultResult
• Image editing by texture replacement
Application 2:Application 2:Temporal TextureTemporal Texture
• Indeterminate motions both in space and time– fire, smoke, ocean waves
• How to synthesize?– extend our 2D algorithm to 3D
Temporal TextureTemporal Texture
Fire Smoke Waves
Input
Result
Future WorkFuture Work
• More general “textures”– light fields, solid textures– motion signals– displacement maps
• Real time texture synthesis
AcknowledgmentAcknowledgment
• Kris Popat• Alyosha Efros• Stanford Graphics Group• Intel, Interval, Sony
More information
http://graphics.stanford.edu/projects/texture/