15-462, Computer Graphics Spring 2004

Programming assignment 4 - Texture Synthesis


This is your last programming assignment and it should be a lot of fun. It is a bit different from your other assignments (and the historic scope of this class) in that it concerns an "image-based" research area. You can throw away your OpenGL reference books. Your textbook briefly mentions image-based rendering in section 13.8, but it doesn't cover what you'll be doing here: texture synthesis.

First, a simple definition of texture synthesis from Ashikhmin 2001

"a texture synthesis method starts from a sample image and attempts to 
produce a texture with a  visual appearance similar to that sample."

Texture synthesis is a very active research area in graphics. It's also a relatively new research area. Its roots might be traced back to the 80's, but the "seminal paper", Heeger and Bergen 1995 is less than a decade old. In my opinion, however, the seminal papers are really Efros and Leung 1999 and Wei and Levoy 2000. These (very similar) methods are elegant, easy to implement, and work better than anything before them. This is the class of algorithm you will be asked to implement. I will refer to these methods as "neighborhood-based" methods. Here's a short description of the algorithms, then we'll explore all of the variations, uses, and details.


A is our input texture. It's a nice texture, but we want more of it. So how do we do this? We could try just tiling it over and over again, but even for periodic textures this can produce visual artifacts. And in general, textures might not tile at all. We want a general method for seamlessly making an arbitrary amount of texture.

We create a data structure called "textons" which we assume are the base units that make up a texture. B shows four of these textons. The texton simply stores the center pixel color and the color distribution around it. We can create a texton from each (non-edge) pixel in the input texture. (Or you could assume the edges of the image are mirrored and define the edge textons that way. Or you could find some way to use partial textons.) We're treating each little image window as an example of valid texture element.

So now we have some data structure containing lots of example textons. This is a memory based learning technique. We have "learned" the input texture by assuming every texton in the input image is a valid output of whatever process generated this texture. Each texton can be thought of as a vector of size equal to the number of pixels in the neighborhood times the number of color channels (12 * 3 in our case). Similarly each vector could represent a point in some very high dimensional space. Different papers will approach textons differently so remember these things are all equivalent.

Why is this neighborhood-based concept of textons useful? Because it encodes the spatial relationship between colors that are important for preserving the structure of the texture.

But, we're still missing an ingredient before we start synthesizing texture. We need a distance metric. We need to be able to quantitatively express how far apart any two textons are. Why? What if our textons are as pictured in B. We want to try laying down pixels to synthesize a new texture, and we stumble upon the situation shown in C. We have some amount of texture that we've already synthesized, and we just need to decide what the next pixel should be. How do we do this? We find the texton in B that is the closest to our current situation. We take the center pixel of that texton and copy its color into the texture we're synthesizing. Then we slide over and repeat until we have as much texture as we want.

Your distance metric could be a lot of things. The simplest thing would be SSD- Sum the squared difference between each corresponding texton element. This actually works reasonably well. However, you might want to give the pixels near the center of the texton more weight. They are probably more important in finding a well matched texton. People do this by weighting the textons with a Gaussian kernel, which is a fancy way of saying that the farther the pixel is from the center of the texton, the less it should contribute to the distance metric. One could also treat the color channels unequally in a distance metric. It would be reasonable to weight green more than red, and red more than blue, since humans have unequal sensitivity to R, G, and B.

Now you can also see why those neighborhoods have a funny shape- it's a side effect of a synthesis algorithm. We didn't bother to keep track of the pixels to the right or below the current pixel because we're going to synthesize in scanline order. We know that the only pixels we'll have synthesized are above and to the left of our current location, so those are the only pixels we should use to decide the next pixel color. This is the approach used in Wei and Levoy 2000. Efros and leung 1999 use a more general method that normalizes the distance metric over any already synthesized pixels, but it is not well suited for optimization.

By using these 5 width textons, we (partially) conserve the spatial coherency of the input texture when creating our new texture. In reality, we only conserve the coherency of texture elements that are smaller than 5 pixels. The above figure shows synthesis results from Wei and Levoy 2000 using varied neighborhood sizes. The smaller the neighborhood, the less spatial information each texton encodes. Your synthesis algorithm is blind to larger scale mistakes it may be making. Unfortunately the neighborhood size necessary to faithfully synthesize a texture varies from texture to texture. It also becomes computationally troublesome to use large neighborhoods- the Big O of this type of synthesis algorithm (without optimization) is O((neighborhood width * neighborhood height) * (input width * input height) * (output width * input height))

Unfortunately this only establishes a baseline texture synthesis algorithm. Considerable improvements result from using multiresolution neighborhoods and acceleration as in Wei and Levoy 2000. You can consult the paper for the details, but I'll give a brief, high-level description of both.

Multiresolution neighborhoods are a way to faithfully reproduce large scale texture elements without having to use a giant neighborhood. A 40x40 neighborhood would be 64 times slower than a 5x5 neighborhood, for instance. A better way to deal with large scale texture structures is to operate at multiple resolutions. For instance, reduce the size of your input and output to 16 pixels by 16 pixels, then synthesize your output. That captured the large scale elements, right? Now expand them both to 32 by 32 and use your 16x16 synthesis output as a starting point. Continue until you're back at the original resolution. In actuality, the algorithms accomplish this not by having pixels from two different image resolutions in the neighborhood at the same time using an image pyramid.

Acceleration refers to several possibilities. In Wei and Levoy 2000, Tree Structured Vector Quantization is used. This sounds confusing but it's really quite simple. Instead of storing all of the input textons in a list that must be brute-force searched every time you want to synthesize a pixel, they create a data structure which instead returns an approximate best match in exchange for being much faster. It's just rounding. Here's a good page that explains Vector Quantization. Another approach is to run PCA on the textons (or rather, their equivalent representation as points in a high dimensional space). PCA basically finds the axes in this high dimensional space along which there is the most variance. By projecting high dimensional points on to this reduced set of bases, you can get by with a smaller coordinate system.

Here are the types of texture synthesis results you can expect from your texture synthesis algorithm.

That page also shows results for "gap-filling" in which texture from one part of an image is found to replace another part. In the case shown it's just a gap, but you can use algorithms like this to remove image elements very seamlessly. It's a bit like an intelligent clone tool in photoshop.

Applications and Extensions

Texture synthesis has a lot of uses. The obvious one, of course, is given in the definition: Making more texture. This is good for texturing 3d environments, characters, etc. I expect to see simple texture synthesis algorithms being programmed on graphics hardware in the near future.

Researchers have stumbled upon many more uses for texture synthesis algorithms. Ashikhmin 2001, a clever extension to neighborhood-based algorithms, does "targetted" texture synthesis (see bottom half of page), in which the synthesis algorithm basically becomes a competition between trying to resemble one input image and trying to use the texture of another input image. Image Analogies extended Ashikhmin in to an analogy framework which can be used to do all sorts of neat things.

Texture synthesis algorithms typically scale to higher dimensions, as well. This means that instead of just operating in two dimensions for an image, it can operate in three dimensions to synthesize movies. You can think of movies as a 3d texture, containing x and y dimensions like an image but with the addition of a time dimension. The texture synthesis algorithm you're programming also scales easily to 3d (though the naive results are generally poor).

Texture synthesis can be done directly onto 3d surface to avoid all of the tricky issues of fitting a planar texture on to an arbitrarily shaped polygon. (Turk 2001, Wei and Levoy 2001).

Graph Cuts, Kwatra et. al. 2003. shows examples of using texture synthesis to merge multiple images seamlessly. The synthesis algorithm can also imitate perspective effects or mirror and rotate textures in order to create less repetitive results. Graph Cuts is probably the best general texture synthesis algorithm right now.

Your Assignment, due 11:59pm, Tuesday December 7

70 points total - You must implement a texture synthesis algorithm meeting the following baseline requirements
  • 10 points Must allow command line specified input texture file, texton neighborhood size, and output size. Texton neighborhood size can be restricted to odd numbers. If you do multiresolution neighborhoods, explain in the readme file how to specify the neighborhood sizes.
  • 15 points Generates textons from the specified input texture.
  • 15 points Employs some distance function that takes as input two textons and returns a distance
  • 20 points Synthesizes a new texture of specified size by finding the minimum distance texton to add at each step of the synthesis, as described above. You will have to worry about initialization and boundary conditions- you can think up something reasonable on your own or do what any of the listed papers have done.
  • 5 points Write the synthesized texture to file.
  • 5 points Good comments and explaination of your code structure.

    10 points - Quality of results

  • If you are able to produce results comparable to Efros and leung 1999, then you're fine.

    10 points - You must make a really cool movie

  • Find a unique texture that your algorithm performs well on, then make a movie of your algorithm synthesizing a larger version of that texture. Write a movie frame after every 20 or so pixels you place.
  • Or, even cooler, while doing this have it show the input texture and draw a line between each pixel as you synthesis it and its position in the input texture. (but make sure the movie is actually watchable)
  • Or, if you noticed that texture synthesis can be used to synthesis anything with a texture-space equivalent (hint: heightfields), you could use one of your earlier assignments to show "heightfield synthesis" results.
  • If you do any extensions, it would certainly be nice to show those, as they might be unique to your work.
  • Or any combination of these or anything else you think is neat. Note - you can turn in as many frames as you want, within reason. Just make sure they're numbered correctly 009.jpg, 010.jpg, etc...

    Extensions (some of these are fairly involved). For each of these that you choose to do, add a paragraph to your readme file explaining how exactly you added each extension. Also you should include a directory of before and after results for each extension you add.

  • 10 points - hole filling / constrained texture synthesis (see bottom of Efros and Leung project page ). This one isn't too hard. In this case you won't start with a blank or noise image for your output texture- you'll actually synthesis on top of an existing texture. You just won't synthesize everywhere. Reserve some color to mark regions that will be overwritten (Red 255, Blue 255, Green 0 is a good one). When building textons, don't build any textons that have that color in them. When synthesizing, replace only pixels that are that color. Otherwise everything is the same. Alternatively you could formulate it by asking for another, binary image as input which will define the regions to fill.
  • 10 points - Coherence parameter. Developed by Ashikhmin and named in Image Analogies. Basically this means adding a "bonus" to coherent textons when deciding which texton is the closest to your current synthesis situation. What is coherent? You'll have to check out the papers. Your coherence parameter should become a command line option.
  • 10 points - Targeted synthesis. From Ashikhman 2001. Now your program takes two inputs. The textons are generated entirely from one, and the synthesis proceeds on top of the other. The textons are square instead of L-shaped. It's really a competition between remaining loyal to the texture from which you generated textons (top half of the texton), and remaining loyal to your "target" (bottom half of the texton). Image Analogies formalizes this by allowing you to change the relative importance of the two halves of your texton neighborhood.
  • 15 points - Multiresolution synthesis. This is well covered in Wei and Levoy 2000. It will require you to create an "image pyramid" and making your textons span multiple levels on that pyramid.
  • 15 points - Analogy based synthesis. It's actually simpler than it seems. Basically, your textons now span multiple images, and each texton now defines a valid neighborhood to neighborhood transformation. See Image Analogies. You don't have to do multiresolution or coherence parameter to do analogy. Your program will need to accept more input images to use the analogy framework.
  • 15 points - Acceleration. Google for Approximate Nearest Neighbor or use this library that Image Analogies used. You don't have to write your own ANN routine. You should put a paragraph in to your readme file explaining how much acceleration you achieved, and at what expense to the quality of your results. Add a command line option to enable or disable acceleration.
  • ?? points - Your own idea or an idea from one of the papers that I haven't listed above. Email me (zhonghh@cs.cmu.edu) first if you want to make sure you'll get credit, or just go ahead and try and impress us. We will consider any novel or fun extensions you create.

    Full credit is 100. With extra credit, max score is 120.

    Notes and Tips

  • Read the papers first! Read both Efros and Leung 1999 and Wei and Levoy 2000 before coding. They cover the same ground, but what's not clear to you in one paper might make more sense in the other. You're not expected to understand every mathematical detail of any of these papers.
  • Likewise, before you try any of the extensions, read the paper in which they are described.
  • Texture Synthesis is an active, relatively new research area. Papers are still being published in SIGGRAPH on texture synthesis and its many offshoots. So it's entirely possible that if you have some clever ideas with your project, they might be worthwhile to the community as a whole. You should be pleased that you're doing cutting edge work!
  • The base synthesis algorithm you code should be entirely your own (+ openGL if you want it, + image handling libraries). When doing extensions, you can use other libraries to help you. There is source code online to do texture synthesis. Both Image Analogies and Ashikhman 2001 are open source. We would prefer you to think of algorithms on your own. Don't panic if your results aren't as good as theirs, they have lots of hidden parameters and pick their examples very carefully. Just try and write the best texture synthesis algorithm you can.
  • Jpeg compression can be a problem, especially if you are trying to do hole filling as described above. Jpeg compression could blur out your colors such that you can't identify your "reserved color". Even if you use a seperate image as a mask, the jpeg blurring could be a problem. So DO NOT use JPEG format for your texture. Instead you can use ppm or tiff format which are uncompressed. The picture library you have used in previous projects can handle ppm and tiff easily. See /afs/cs/academic/class/15462/include/pic.h for details. Noted that for input texture, JPEG is ok, but for mask and output texture, JPEG is not good.

    Some Test Images

    Here are some images you may use for your test. Please do find some other cool images for your result.

  • References:

    Ashikhmin, M. 2001. Synthesizing natural textures. 2001 ACM Symposium on Interactive 3D Graphics (March), 217226.

    Efros, A., and Freeman, W. T. 2001. Image quilting for texture synthesis and transfer. Proceedings of SIGGRAPH 2001 (August), 341 346.

    Efros, A., and Leung, T. 1999. Texture synthesis by non-parametric sampling. In International Conference on Computer Vision, 10331038.

    Heeger, D. J., and Bergen, J. R. 1995. Pyramid-based texture analysis/ synthesis. Proceedings of SIGGRAPH 95 (August), 229238.

    Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., Salesin, D. 2001. Image Analogies. Proceedings of SIGGRAPH 2001 (August).

    Kwatra, V., Schdl, A., Essa, I., Turk, G. and Bobick, A. 2003. Graphcut Textures: Image and Video Synthesis Using Graph Cuts. Proceedings of SIGGRAPH 2003 (August).

    Turk, G. Texture Synthesis on Surfaces. Proceedings of SIGGRAPH 2001 (August), 347-354.

    Wei, L.-Y., and Levoy, M. 2000. Fast texture synthesis using treestructured vector quantization. Proceedings of SIGGRAPH 2000 (July). 479488.

    Wei, L.-Y., and Levoy, M. 2001. Texture Synthesis over Arbitrary Manifold Surfaces. Proceedings of SIGGRAPH 2001 (August).