appearance-guided synthesis of element arrangements by example
DESCRIPTION
Appearance-guided Synthesis of Element Arrangements by Example. Thomas Hurtut , Pierre- Edouard Landes , Joëlle Thollot Yann Gousseau Remy Drouilhet , Jean-Fran ç ois Coeurjolly. Motivation. Mucha , The Lady of the Camellias , 1896. Hokusai, Mount Fuji in Clear Weather , 1823. - PowerPoint PPT PresentationTRANSCRIPT
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Appearance-guided Synthesisof Element Arrangementsby Example
Thomas Hurtut, Pierre-Edouard Landes, Joëlle ThollotYann GousseauRemy Drouilhet, Jean-François Coeurjolly
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MotivationMucha, The Lady of the Camellias, 1896
Klimt, Portrait of Adele Bloch-Bauer I, 1907
Hokusai, Mount Fuji in Clear Weather, 1823
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Our Goals
Vector-based inputs
By-example approach
Controllable synthesis
Capture of non-uniform distributions
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VectorRaster
Our method
Ijiri et al. ‘08 Barla et al. ’06 RRBarla et al. ’06 EGExtension to vector primitives
• Unconstrained positions• More than pixel colors
StatisticalmodelingGuo et al. ‘01
Statistics matchingPortilla et al. ‘01
Patch-basedEfros et al. ‘01Dischler et al. ’02Kwatra et al. ‘03
Pixel-basedEfros et al. ‘99Ashikhmin et al. ‘01Lefebvre et al. ‘06
Non-parametric Parametric
Example-based Texture Synthesis
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Local neighborhood matching
Our method
Non-parametric Parametric
Example-based Texture Synthesis
Hexagonal distributionsNear-regular to
random distributions
Global analysis
Regularity
Pixel grid Delaunay triangulation
Ijiri et al. ‘08 Barla et al. ’06 RRBarla et al. ’06 EG
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Our method
Our Method
• Statistical analysis of the element distribution
• Model the spatial interactions between elementsOur model = multi-type point processes
• But, many model parameters from limited input
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Our Method
RealizationModel fitting
Untractable if continuous interactions considered
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Our Method
Categorization RealizationModel fittingModel fitting Realization
Untractable if continuous interactions considered
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Our Method
Categorization RealizationModel fitting
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Simplification by Categorization
2 perceptual principles:• Law of similarity [Kohler ‘76]
Visually-similar elements perceived as a unit
• Texton discrimination [Julesz ‘86]Visually-dissimilar elements pre-attentively discriminated
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Element Description
Area
Orientation
Elongation
# Extremities
# Crossings
Area
Orientation
Elongation
# Extremities
# Crossings
Descriptive features
Curve-based elements
Inspired from Julesz’ perceptual studies
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Feature Analysis
Reduction to 1D
AreaArea Orientation Elongation #Extremities #CrossingsOrientation Elongation #Extremities #Crossings
2-fold analysis
Categorization by a contrario mode-seeking
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Categorization Results
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Categorization Results
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Categorization Results
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Our Method
RealizationModel fitting
Categorization
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Our Method
RealizationModel fitting
Categorization
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Modeling the Spatial Interactions
How often? At what distance?
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Modeling the Spatial Interactions
At what distance?How often?
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Interactions Between Categories
Strauss hard-core interaction model
Let us consider the term
distance
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Parameter Estimation (details in paper)
• Pseudo-likelihood maximization
• Minimal pair-wise Euclidean distance
• Analysis of the Ripley function
How often?
At what distance?
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Our Method
Realization
Categorization Model fitting
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Our Method
Realization
Categorization Model fitting
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Arrangements as Model Samples
Monte-Carlo Markov Chain sampling
Iterative procedure: random elementary perturbations
timesteps
Output
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Arrangements as Model Samples
timesteps
Output
Birth Death
Monte-Carlo Markov Chain sampling
Iterative procedure: random elementary perturbations
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Arrangements as Model Samples
timesteps
Birth Death
Output
Monte-Carlo Markov Chain sampling
Iterative procedure: random elementary perturbations
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Arrangements as Model Samples
timesteps
Output
Monte-Carlo Markov Chain sampling
Iterative procedure: random elementary perturbations
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Our Method
Categorization Model fitting Realization
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Output
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Output
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Output
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Output
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Comparison with Related Work
Barla et al. ‘06 Ours
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OursBarla et al. ‘06
Comparison with Related Work
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Comparison with Related Work
OursBarla et al. ‘06
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Element Density Control
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Element Density Control
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Barla et al. ‘06OursMucha, The Lady of the Camellias, 1896
« Emulating the Masters »
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« Emulating the Masters »
Hokusai, Mount Fuji in Clear Weather, 1823
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Over-categorization
Output
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Handling of Regularity
Output
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Interactions Between Centroids
Output
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Future Work & Conclusions
• Possible improvements• Higher-order interactions for regularity• Better representation for anisotropic elements• Quality-driven stopping criterion for faster synthesis
• Contributions• Global input analysis• Appearance-driven synthesis• Wider range of distributions supported
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Behind the Scenes (Parameter Estimation)
Model to fit
Full arrangement
Estimation ofby log-pseudo-likelihood maximization
IntuitionStatistical “explanation” of the element positions
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Behind the Scenes (Parameter Estimation)
Model to fit
Full arrangement
Estimation of by Ripley function minimization
IntuitionEvaluate distance where element distribution gets close to random
If distribution purely random
If distribution more regular