Efficient Editing of Aged Object Textures
By:
Olivier Clément
Jocelyn Benoit
Eric Paquette
Multimedia Lab
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Introduction
Realistic image synthesis Virtual reality, video games, special effects,
etc. Aging (or weathering)
Many effects Many objects Time consuming
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IntroductionRedesign iterations
Produces theappropriate texture
Visualizes theappearance of an object
Reviewsthe result
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Objectives
To build a system To edit aging effects on textures To increase realism To reduce the amount of work Adapted for artists
adequate control interactive no complex parameters
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Previous Work
Physically based methods[Dorsey and Hanharan 2000; Merillou et al. 2001; O’Brien et al. 2002; etc.]
Highly realistic results but lengthy calculations Non-intuitive physical parameters
Empirical methods[Chain et al. 2005; Gobron and Chiba 2001; Paquette et al. 2002; etc.]
More intuitive parameters Both approaches
Do not provide the control required by artists Target a single aging effect
Aging methods
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Previous Work
Image based[Gu et al. 2006; Wang et al. 2006; etc.]
Capture the time-varying aspects of the material Similar to our approach
Focus of our approach Simple capture process Adequate control
Aging methods
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Aging Editing Process
Source image Image, photograph Containing aging
effects Target aging mask
Binary image Desired pattern
Reproduction image New aging effects
Process overview
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Aging Editing Process
Segmentation phase Semi-automatic Aged regions
Elimination phase Automatic Aging removed
Reproduction phase Automatic New aging effects
Phase description
Red
esig
n it
erat
ion
s
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Aging Editing ProcessImages summary
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Segmentation Phase
Identifies aged regions
Could be done with Segmentation tools Image editing software
Stroke-based technique Lischinski et al. [2006]
Worked efficiently for semi-automatic identification
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Segmentation PhaseStroke-base technique - Video
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Elimination Phase
Constrained texture synthesis Match the non-aged neighbourhood
Search using ANN library Arya et al. [1998]
The algorithm
best match
…
newbest match
Elimination image Source image
copy thepixel color
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Elimination Phase
The boundary pixels Non-aged pixels in
their neighbourhood Must be filled first
The aged region is filled iteratively
Hole-filling
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Outline
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Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Reproduction Phase
Extension of the elimination algorithm
Consider the aged / non-aged context
The new term
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Reproduction Phase
Does not synthesize the entire image
Only specified regions
Iterative construction from multiple source images
Aging effects transfer and combination
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Results
Source image Elimination image Reproduction imageSource aging mask Target aging mask
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ResultsSource image Elimination image Reproduction image
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ResultsSource image Elimination image Reproduction image
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ResultsSource image Aging masks Reproduction image
More results in thepaper and the video…
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Results
User interaction is minimal Interactive computation time Efficient for redesign iterations
Efficiency
2.5 minutes - once25 seconds - once2 minutes
every iteration3 seconds
every iteration
Obtained on a PC with 3.2 GHz CPU and 3GB of RAM
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Limitations
Apply only on surfaces No fractures or deformations
Camera-based texture acquisition Specular lighting Surface distortion
Current implementation Interactive on textures up to 512 x 512
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Outline
Objectives Previous Work Aging Editing Process
Segmentation Phase Elimination Phase Reproduction Phase
Results and Limitations Conclusion
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Conclusion
A framework To edit aging effects on textures To reduce the amount of work needed during
the redesign iterations Benefits
Appropriate for artists adequate control and interactivity no complex parameters
Works well for several types of aging effects
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Conclusion
Synthesize the target aging mask For numerous regions Ex: scratches
Handle layers in effects combination Multiple effects over the same regions Ex: dirt on top of rust
Faster synthesis To handle higher resolution textures
Future work
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? We would like to thank :
And all our reviewers…
Questions
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Previous Work
Texture synthesis[Efros 1999; Hertzmann 2001; Kwatra 2003; Lefebvre 2006; Liang 2001; etc.]
Synthesis based on neighbourhood matching
Our system Extends from these algorithms Specializes for the aging context
Texture synthesis
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Previous Work
Image analogies, Hertzmaan et al. [2001] The output image is completely synthesized Our approach uses a similar algorithm that
synthesize only regions of the output Our approach should be considered as an
extension
Texture synthesis
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Elimination Phase
The replacement pixel is : Selected from the non-aged pixels of the source
image One of the best neighbourhood matches
The system seeks a replacement pixel that minimizes the following L2 norm :
The replacement pixel
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Elimination Phase
An exhaustive search would require processing time far from interactive
Thus, an approximation of the best match is found with the ANN library (Arya et al. [1998]) Approximate nearest neighbour searching algorithm
based on a kd-tree structure Our feature vector is composed of the RGB
components of the non-aged pixels around the pixel to replace
Interactivity
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