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Unformatted text preview: + = Image Quilting for Texture Synthesis & Transfer
Alexei Efros (UC Berkeley) Bill Freeman (MERL) The Goal of Texture Synthesis
input image SYNTHESIS True (infinite) texture generated image Given a finite sample of some texture, the goal is to synthesize other samples from that same texture
• The sample needs to be "large enough“ 1 The Challenge Need to model the whole spectrum: from repeated to stochastic texture repeated stochastic Both? Texture Synthesis for Graphics
Inspired by Texture Analysis and Psychophysics • [Heeger & Bergen,’95] • [DeBonet,’97] • [Portilla & Simoncelli,’98] …but didn’t work well for structured textures • [Efros & Leung,’99]
• (originally proposed by [Garber,’81]) 2 Efros & Leung ’99
[Shannon,’48] proposed a way to generate Englishlooking text using Ngrams: • Assume a generalized Markov model • Use a large text to compute prob. distributions of
each letter given N1 previous letters • Starting from a seed repeatedly sample this Markov
chain to generate new letters • Also works for whole words WE NEED TO EAT CAKE Mark V. Shaney (Bell Labs)
Results (using alt.singles corpus): alt.singles • “As I've commented before, really relating to
someone involves standing next to impossible.” • “One morning I shot an elephant in my arms and
kissed him.” • “I spent an interesting evening recently with a grain
of salt” Notice how well local structure is preserved! • Now, instead of letters let’s try pixels… 3 Efros & Leung ’99
nonparametric sampling p
Input image Synthesizing a pixel Assuming Markov property, compute P(pN(p))
• Building explicit probability tables infeasible • Instead, let’s search the input image for all similar
neighborhoods — that’s our histogram for p To synthesize p, just pick one match at random Efros & Leung ’99
The algorithm • Very simple • Surprisingly good results • Synthesis is easier than analysis! • …but very slow Optimizations and Improvements • [Wei & Levoy,’00] (based on [Popat & Picard,’93]) • [Harrison,’01] • [Ashikhmin,’01] 4 Chaos Mosaic [Xu, Guo & Shum, ‘00] input idea result Process: 1) tile input image; 2) pick random blocks and place them in random locations 3) Smooth edges Used in Lapped Textures [Praun et.al,’00] Chaos Mosaic [Xu, Guo & Shum, ‘00] input result Of course, doesn’t work for structured textures 5 Image Quilting
Idea: • let’s combine random block placement of Chaos
Mosaic with spatial constraints of Efros & Leung Related Work (concurrent): • Realtime patchbased sampling [Liang et.al. ’01] • Image Analogies [Hertzmann et.al. ’01] Efros & Leung ’99 extended
nonparametric sampling p B
Input image Synthesizing a block Observation: neighbor pixels are highly correlated Idea: unit of synthesis = block
• Exactly the same but now we want P(BN(B)) • Much faster: synthesize all pixels in a block at once • Not the same as multiscale! 6 block Input texture B1 B2 B1 B2 B1 B2 Random placement of blocks Neighboring blocks constrained by overlap Minimal error boundary cut Minimal error boundary
overlapping blocks vertical boundary _ 2 =
min. error boundary overlap error 7 Our Philosophy
The “Corrupt Professor’s Algorithm”: • Plagiarize as much of the source image as you can • Then try to cover up the evidence Rationale: • Texture blocks are by definition correct samples of
texture so problem only connecting them together 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
• use dynamic programming to compute minimal error
boundary cut 8 9 10 11 Failures
(Chernobyl Harvest) 12 Texture Transfer
Take the texture from one object and “paint” it onto another object • This requires separating texture
and shape That’s HARD, but we can cheat • Assume we can capture shape by
boundary and rough shading Then, just add another constraint when sampling: similarity to underlying image at that spot parmesan +
rice = = + 13 + = + = 14 Source texture Target image Source correspondence image Target correspondence image + = 15 Portilla & Simoncelli Xu, Guo & Shum input image Wei & Levoy Image Quilting Portilla & Simoncelli Xu, Guo & Shum input image Wei & Levoy Image Quilting 16 Homage to Shannon! Portilla & Simoncelli Xu, Guo & Shum input image Wei & Levoy Image Quilting Conclusion
Quilt together patches of input image • randomly (texture synthesis) • constrained (texture transfer) Image Quilting • No filters, no multiscale, no onepixelatatime! • fast and very simple • Results are not bad 17 ...
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This note was uploaded on 11/06/2010 for the course CSE 527 taught by Professor Ab during the Fall '09 term at Cornell University (Engineering School).
 Fall '09
 Ab

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