image-guided weathering: a new approach applied to flow phenomena
DESCRIPTION
Image-Guided Weathering: A New Approach Applied to Flow Phenomena. C. Bosch 1 , P. Y. Laffont, H. Rushmeier, J. Dorsey, G. Drettakis Yale University – REVES/INRIA Sophia Antipolis 1 Currently at ViRVIG, University of Girona. Aging and Weathering. Essential for modeling urban environments - PowerPoint PPT PresentationTRANSCRIPT
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Image-Guided Weathering:Image-Guided Weathering:A New Approach Applied to Flow A New Approach Applied to Flow
PhenomenaPhenomenaC. BoschC. Bosch11, P. Y. Laffont, H. Rushmeier, , P. Y. Laffont, H. Rushmeier,
J. Dorsey, G. DrettakisJ. Dorsey, G. Drettakis
Yale University – REVES/INRIA Sophia AntipolisYale University – REVES/INRIA Sophia Antipolis1 1 Currently at ViRVIG, University of GironaCurrently at ViRVIG, University of Girona
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Aging and WeatheringAging and Weathering
Essential for modeling urban environments Governed by physical, chemical and biological processes
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Flow effectsFlow effects
Particularly complex Flow over the scene (global effect)
Material properties (local effect)
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Aging and Weathering in CGAging and Weathering in CG
Physically-based simulation Difficult to get the desired effect
Texture synthesis Restricted by input information
Global effects particularly hard
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MotivationMotivation
Physically-based simulation More flexible, allows global effects
Two main difficulties Choosing appropriate parameters to achieve a given effect
Obtaining realistic visual detail
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Image-Guided WeatheringImage-Guided Weathering
Use images to guide simulation Flow stains as a representative case
Exemplar
New simulation
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Overview (I)Overview (I)
Extract data from exemplars Color information
Simulation parameters
High frequency details
Si = 1.301 rt = 0.252kS = 0.0201 at = 0.404kD = 0.0807 T = 803ka,t = 0.021
Exemplar
Data
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Overview (II)Overview (II)
Simulate new effects on scenes
Si = 1.301 rt = 0.252kS = 0.0201 at = 0.404kD = 0.0807 T = 803ka,t = 0.021
Data
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Related WorkRelated Work
Simulation Phenomenon-specific [Merillou08]
Flow stains [Dorsey96; Chen05; Endo10]
Capture-and-transfer (synthesis) Single image [Wang06; Xue08]
Acquisition systems [Gu06; Mertens06; Sun07; Lu07]
Inverse procedural textures [Bourque04; Lefebvre00]
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Flow modelFlow model
Particle-based simulation [Dorsey96] Absorption, solubility and deposition
Stain concentration maps
Parameters Particles: mass (m), Si
Stain material: kS, kD
Target materials: a, ka, roughness (r)
Simulation: time (t), particle rate (N)
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Extracting StainsExtracting Stains
Based on Appearance Manifolds [Wang06]
ExemplarAppearance Manifold
Degree Map
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Degree map = Stain concentration map
Error Simulation
Parameter FittingParameter FittingInput stain Degree map
Si = 1kS = 0.04kD = 0.04rt = 0.2at = 0.3ka,t = 0.05T = 300
Si = 1kS = 0.04kD = 0.04rt = 0.2at = 0.3ka,t = 0.05T = 300
Initialparameters
New parameters
Si = 1.3kS = 0.02kD = 0.08rt = 0.25at = 0.4ka,t = 0.02T = 803
Si = 1.3kS = 0.02kD = 0.08rt = 0.25at = 0.4ka,t = 0.02T = 803
Error < threshold or max. iterations
StopStop
Proxy geometry
target
(Levenberg-Marquardt) [Lourakis04]
image plane
source
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Improving FittingImproving Fitting
1. Stain distribution along the source Accumulate degree from bottom to top
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Improving Fitting (II)Improving Fitting (II)
2. Flow deflection along the target Compute local degree distribution (~vector field)
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Error Simulation
Input stain Degree map
Si = 1kS = 0.04kD = 0.04rt = 0.2at = 0.3ka,t = 0.05T = 300
Si = 1kS = 0.04kD = 0.04rt = 0.2at = 0.3ka,t = 0.05T = 300
Initialparameters
New parameters
Si = 1.3kS = 0.02kD = 0.08rt = 0.25at = 0.4ka,t = 0.02T = 803
Si = 1.3kS = 0.02kD = 0.08rt = 0.25at = 0.4ka,t = 0.02T = 803
Error < threshold or max. iterations
StopStop
Proxy geometry
target
(Levenberg-Marquardt) [Lourakis04]
image plane
source
Parameter Fitting (II)Parameter Fitting (II)
Vector field
Stain distribution
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Fitting Results (w/o vector field)Fitting Results (w/o vector field)
Exemplar Degree Map SimulationUsing source distribution
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Fitting Results (w/o vector field)Fitting Results (w/o vector field)
Exemplar Degree Map Simulation
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Fitting Results (w/ vector field)Fitting Results (w/ vector field)
Exemplar
Degree Map Simulation w/o vfield
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Fitting Results (w/ vector field)Fitting Results (w/ vector field)Exemplar Degree Map Simulation
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Fitting Results (w/ vector field)Fitting Results (w/ vector field)Exemplar Degree Map Simulation
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Fitting Results (Complex Targets)Fitting Results (Complex Targets)
Exemplar Degree Map Simulation
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Stain DetailStain Detail
Simulation lacks spatial variations (high-frequency detail)
Degree Map Simulation
Exemplar
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Detail MapsDetail Maps
Extract detail by image difference Use guided texture synthesis [Lefebvre05] Detail maps will modify stain adhesion
Degree Map Simulation Difference
Detail Map
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Simulating New StainsSimulating New Stains
Link data to stain sources and targets Parameters, detail maps, color
Use 1D texture synthesis for distributions
Run flow simulation Flow deflected by target geometry (+ disp. map)
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Color TransferColor Transfer
Transfer stain color from input image Background mixed with stain everywhere
Non-linear relationship between color and degree
Use per-pixel warping
background color
target background
fully stained
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ResultsResults
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Results (II)Results (II)
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Results (III)Results (III)
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Results (IV)Results (IV)
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PerformancePerformance
Preprocessing Degree map: 1-3 minutes
Fitting: 30-60 minutes (500 iter., ~256x512)
Detail synthesis: 1-2 minutes (1024x1024)
Final simulation Stain simulation: 2-5 minutes/stain
Color warping: 5-8 seconds/stain (1024x1024)
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LimitationsLimitations
Good extraction from background Fitting: Not true physical estimations Detail maps: Depend on appropriate fit Computation time
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ConclusionsConclusions
New approach to acquire simulation data from photographs Solves parameter estimation from images
Combines simulation with data-driven methods Appearance manifold, texture synthesis, …
Fills the gap between data-driven and simulation
Easy to use
Natural variations (including global effects)
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Future workFuture work
Extend to other weathering phenomena Deal with large scale scenes
Fast simulation, global effects, …
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AcknowledgementsAcknowledgements
Visiting grant U.Girona ANR project (ANR-06-MDCA-004-01) ERCIM “Alain Bensoussan” Fellowship Autodesk (Maya/MentalRay) Coding help: Li-Ying, Su Xue Scene treatment: S. Close and F. Andrade-Cabral
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Thank youThank you