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Overview of Texture Synthesis

Overview of Texture Synthesis. Ganesh Ramanarayanan Cornell Graphics Seminar. ???. small texture. big texture. The Problem. ???. small texture. big texture. A Simple Solution …. small texture. A Simple Solution … Tiling. ???. big texture. A Simple Solution … Tiling.

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Overview of Texture Synthesis

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  1. Overview of Texture Synthesis Ganesh Ramanarayanan Cornell Graphics Seminar

  2. ??? small texture big texture The Problem

  3. ??? small texture big texture A Simple Solution …

  4. small texture A Simple Solution … Tiling ??? big texture

  5. A Simple Solution … Tiling small texture Tiling is obvious! big texture?

  6. Ideal Solution?

  7. The Problem … in Words • Given texture I, generate a texture J which • Looks like the same texture • Has no obvious copying or tiling from I • Difference between I and J should be the same as the way I “differs from itself” [DeBonet97] • Things to watch for: • ‘Looks the same’: what is the texture model? • ‘Obvious copying’: how is it avoided? • Underlined text: indicates algorithm parameter

  8. Classes of Algorithms • Multiresolution pyramids • [HeegerBergen95] • Pixel-by-pixel synthesis • [EfrosLeung99] • Multiresolution pixel-by-pixel • [DeBonet97], [WeiLevoy00], [Hertzmann et.al. 01], [Ashikhmin01] • Patch quilting • [EfrosFreeman01], [Kwatra et.al. 03], [WuYu04] • Geometric feature matching • [WuYu04], [Liu et.al. 04]

  9. Heeger Bergen 1995 • Seminal paper that introduced texture synthesis to the graphics community • Algorithm: • Initialize J to noise • Create multiresolution pyramids for I and J • Match the histograms of J’s pyramid levels with I’s pyramid levels • Loop until convergence • Can be generalized to 3D

  10. Heeger Bergen 1995 - Algorithm • Image pyramids • Gaussian • Laplacian • Steerable pyramids [SimoncelliFreeman95] • b): multiple scales of oriented filters • c): a sample image • d): results of filters in b) applied to c)

  11. I I J J Heeger Bergen 1995 - Results Successes Failures

  12. Heeger Bergen 1995 - Results

  13. Heeger Bergen 1995 - Verdict • Texture model: • Histograms of responses to various filters • Avoiding copying: • Inherent in algorithm • No user intervention required • Captures stochastic textures well • Does not capture structure • Lack of inter-scale constraints

  14. De Bonet 1997 • Propagate constraints downwards by matching statistics all the way up the pyramid • Feature vector:multiscale collection of filter responses for a given pixel • Algorithm: • Initialize J to empty image • Create multiresolution pyramids for I and J • For each pixel in level of J, randomly choose pixel from corresponding level of I that has similar feature vector

  15. De Bonet 1997 - Algorithm • 6 feature vectors shown • Notice how they share parent information

  16. De Bonet 1997 - Results

  17. De Bonet 1997 - Verdict • Texture model: • Feature vector containing multiscale responses to various filters • Avoiding copying: • Random choice of pixels with ‘close’ feature vectors, but copying still frequent on small scale • Individual per-filter thresholds are cumbersome • Feature vectors used in later synthesis work

  18. Efros Leung 1999 • Markov Random Fields: pixels synthesized probabilistically based on causal neighborhood • Algorithm: • Initialize J to empty • Synthesize new pixels in a spiral pattern • Select new pixel from I based on conditional probabilities given the current neighborhood in J and similar neighborhoods in I

  19. Efros Leung 1999 - Algorithm I J ‘causal’ neighborhood

  20. Efros Leung 1999 - Results

  21. Efros Leung 1999 – Verdict • Texture model: • MRF • Avoiding copying: • MRF • Neighborhood size = largest feature size • Markov model is surprisingly good • “I spent an interesting evening recently with a grain of salt.” • Search is very slow with large neighborhoods

  22. Wei Levoy 2000 • Adds tree-structured vector quantization speedup and multiresolution matching • Algorithm: • Initialize J to noise • Synthesize new pixels in scanline order • Select new pixel from I that has the closest matching neighborhood to J • In multiresolution case, look instead at neighborhood of feature vectors

  23. Wei Levoy 2000 - Algorithm • Scanline synthesis with neighborhoods • a) input texture • b) algorithm start • c) midway point • d) ending

  24. Wei Levoy 2000 - Results

  25. Wei Levoy 2000 - Verdict • Texture model: • Multiscale filters and MRF • Avoiding copying: • Not explicitly, but in practice inherently avoided; TSVQ adds additional unpredictability • Results comparable to [EfrosLeung99] • TSVQ speedup comes at a price (quality loss)

  26. Ashikhmin 2001 • Adds more verbatim image copying and user-constrained synthesis • Algorithm: • New array J* stores location in I of J’s pixels • Initialize J to empty, J* to random • Synthesize new pixels in scanline order • Select new pixel by copying from patches of I, as determined by locations in J* • In user-constrained case, also include error comparison with user-specified target image T

  27. Ashikhmin 2001 - Algorithm • Candidates: • x=4, y=5 • x=18, y=16 • x=11, y=12 x=3 y=4 x=4 y=4 x=12 y=11 x=17 y=16 x=18 y=16 J*

  28. Ashikhmin 2001 - Results

  29. Ashikhmin 2001 - Verdict • Texture model: • MRF • Avoiding copying: • Actually, here it is encouraged on a small scale, but in practice it doesn’t occur on a large scale • Recognized that copying preserves fine detail • Features can get ‘cut off’ because of the abrupt jumping from patch to patch

  30. Hertzmann et. al. 2001 • Framework for solving image analogies: learning an image filter f by example • Given A, f(A), and B, generate f(B) • To handle texture synthesis, ignore A, B, and let f(A) = I, f(B) = J • Algorithm: • Bring image histograms into correspondence • Combine [WeiLevoy00] and [Ashikhmin01] Each search method is weighted appropriately and then the best choice is used

  31. Hertzmann et. al. 2001 - Algorithm • A provides set of constraints • Histogram matching critical because of color differences between A and B sets of images

  32. I I J J Hertzmann et. al. 2001 - Results

  33. Hertzmann et. al. 2001 - Results

  34. Hertzmann et. al. 2001 - Verdict • Texture model: • Multiscale filters and MRF • Avoiding copying: • See [WeiLevoy00] and [Ashikhmin01] • Unique approach to image manipulation and texture synthesis • Same fundamental limitations as [WeiLevoy00] and [Ashikhmin01]

  35. Efros Freeman 2001 • Inspired by chaos mosaics [Xu et. al. 00] • Make an ‘image quilt’ by pasting together patches of the input and fixing seams • Algorithm: • Initialize J to empty • Copy new patches of certain size in scanline order, with fixed overlap width • Randomly select new patch that has error in overlap region less than given threshold • Copy pixels based on minimal error boundary cut

  36. B1 B1 B2 B2 Neighboring blocks constrained by overlap Minimal error boundary cut Efros Freeman 2001 - Algorithm Input texture block

  37. Efros Freeman 2001 - Results

  38. Efros Freeman 2001 - Verdict • Texture model: • ?? • Avoiding copying: • Randomized patch selection, but still noticeable • Patch size is a hard parameter to understand • Results are surprisingly good given algorithm • Multiscale goes on a brief hiatus

  39. Kwatra et. al. 2003 • Generalizes seam computation in overlap regions as a graph cut problem • Based on [Boykov et. al. 99] (with Ramin Zabih) • Algorithm: • Initialize J to empty • Copy pieces of I to J using a variety of methods • Formulate flow graph in overlap region based on errors and compute minimum cut • Copy sink-side pixels to J • Variety of strategies to further hide seams

  40. Kwatra et. al. 2003 - Algorithm (assume cut region is 3x3 for simplicity)

  41. Kwatra et. al. 2003 - Results

  42. Kwatra et. al. 2003 - Results

  43. Kwatra et. al. 2003 - Verdict • Texture model: • MRF • Avoiding copying: • Even with a multitude of patch selection methods, still noticeable when it happens repeatedly • Paper presents a bag of synthesis tricks without much intuition for when to use what • Graph cut formalization is useful and powerful

  44. Wu Yu 2004 • Synthesizes a feature image J* in parallel • Tries to match features in overlap region • Algorithm: • Compute feature image I* • Initialize J* to empty • Synthesize J and J* in parallel • When searching for new patch, add in weighted feature matching error • Prior to copying, warp selected patch to match features exactly

  45. Wu Yu 2004 - Algorithm • Match feature pixels based on • Tangent orientation • Distance • Bijectivity • Example: • L1out matches L1in • L2out matches L3in • Warping will move matching features closer to each other Feature correspondence

  46. Wu Yu 2004: Results

  47. Wu Yu 2004: Verdict • Texture model: • Feature lines and MRF • Avoiding copying: • No; in fact, mirroring is often very noticeable • Unclear how to combine this metric with others • Insight to use warping is valuable, although the next paper does a much better job with it

  48. Liu et. al. 2004 • Regular textures: can be tiled • Near-regular textures: statistical distortion of a regular texture • Algorithm (simplified): • Mark lattice points in I • Transform I into regular texture R • Express transformation as deformation texture I* • Synthesize larger deformation J* • Synthesize J by combining J* with R

  49. Liu et. al. 2004 - Algorithm I R Deformation Texture I*

  50. Liu et. al. 2004 - Results

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