Image quilting for texture synthesis and transfer
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Image Quilting for Texture Synthesis and Transfer. Alexei A. Efros (UC Berkeley) William T. Freeman (MERL) Siggraph01 ’. About author?. Alexei A. Efros Assistant Professor Computer Science Department & The Robotics Institute School of Computer Science

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Image Quilting for Texture Synthesis and Transfer

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Image Quilting for Texture Synthesis and Transfer

Alexei A. Efros (UC Berkeley)

William T. Freeman (MERL)

Siggraph01’


About author?

  • Alexei A. Efros

  • Assistant Professor

  • Computer Science Department

  • & The Robotics Institute

  • School of Computer Science

  • Carnegie Mellon University

  • From St. Petersburg, Russia

  • Got PhD from UC Berkeley in 2003

  • Then a year as a Visiting Research Fellow in Visual

  • Geometry Group of Oxford

  • Joined in CSD and RI in autumn of 2004

  • Computer graphics & computer vision


About author?

  • William T. Freeman

  • Professor of Electrical Engineering &

  • Computer Science at the

  • Artificial Intelligence

  • Laboratory at MIT(September, 2001)

  • Received a BS in physics and MS in electrical

  • engineering from Stanford (1979), and an MS

  • in applied physics from Cornell(1981)

  • Got his PhD in 1992 from the MIT

  • Worked at Mitsubishi Electric Research Labs (1992 – 2001,

  • Cambridge)

  • Computer vision


+

=

Quilting? Transfer?


Image vs. Texture


Example-based Texture Synthesis

Input Example


input image

SYNTHESIS

True (infinite) texture

generated image

The Goal of Texture Synthesis

Same in perceptual sense


The Challenge

  • Texture analysis: how to capture the essence of texture?

  • Need to model the whole spectrum: from repeated to stochastic texture

Repeated

Stochastic

Both?


Related Work

  • Local region-growing method

  • -Pixel-based

  • -Patch-based

  • Global optimization-based method

  • Heeger and Bergen sig95,Pyramid-based texture synthesis

  • Paget and Longstaff IEEE Tran… 98,Texture synthesis via a noncausal nonparametric multiscale markov random field

  • …..

  • Physical Simulation et al


B1

B1

B2

B2

Neighboring blocks

constrained by overlap

Minimal error

boundary cut

block

Input texture

B1

B2

Random placement

of blocks


2

_

=

overlap error

min. error boundary

Minimum Error Boundary Cut

overlapping blocks

vertical boundary


Minimum Error Boundary Cut


The Image Quilting Algorithm

  • Pick size of block and size of overlap

  • Synthesize blocks in raster order

  • Search input texture for block that satisfies overlap constraints (above and left)

    • Easy to optimize using NN search [Liang et.al., ’01]

  • Paste new block into resulting texture

    • Compute minimal error boundary cut


Synthesis Results


Synthesis Results


Synthesis Results


Synthesis Results


Synthesis Results


Synthesis Results


Portilla & Simoncelli

Xu, Guo & Shum

input image

Wei & Levoy

Image Quilting


Portilla & Simoncelli

Xu, Guo & Shum

input image

Wei & Levoy

Image Quilting


Portilla & Simoncelli

Xu, Guo & Shum

input image

Wei & Levoy

Image Quilting


Failures


Image Quilting vs. Graph Cut

Input

Image Quilting

Graph Cut (siggraph 03’)


Luminance Constraint

+

Texture Transfer


parmesan

+

=

rice

+

=


Conclusion

  • No multi-scale, no one-pixel-at-a-time!

  • fast and very simple

  • Improved stability

  • Results are not bad


Thanks a lot!

Happy New Year!


Pixel-based Methods

  • Compare local causal neighbourhoods

  • Efros and Leung (ICCV ’99)Wei and Levoy (Siggraph 2000)Ashikhmin (I3D 2001)

Input

Output


Patch-based Methods

  • Copy patches of pixels rather than single pixels

    Chaos Mosaic, Xu et al, 1997

    Patch-Based Sampling, Liang et al(ACM 2001)

    Image Quilting, Efros and Williams(Siggraph 2001)


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