#texture generation

[patternv0mit]

Genetica 4.0 Animated Textures

Some default animated seamless textures dating from around 2007 - 2008 that are included in the demo for Genetica 4.0 (Pro Edition), which is an obsolete seamless texture generation software that you can no longer buy keys for (these were rendered on a free trial, and on a totally unrelated note you have the ability to do something really funny if you brought the software before Spiral Graphics went defunct) These seamless animated textures are all credited either to Spiral Graphics (the now defunct company that created Genetica), or some mysterious user known only as Sethren who contributed (their?) textures at June 14th 2007.

[procedural-generation]

Turbulent Planet Textures

One of the images above is from the amazing photos of Jupiter that have been captured by the Juno mission. The other is from a presentation by Stephen M. Cameron, about procedurally generating textures for gas giants that look appropriately swirly.

This effect is tough to replicate with some of the common texturing approaches. And it’s not just for Jovian planets: Earth’s clouds look wrong without it, and there’s a whole host of smaller-scale texturing (like water or flames) that looks better with it.

Perlin noise is a common basis for a lot of texturing, but despite looking a bit like puffy clouds it isn’t all that great for representing textures that are swirling or turbulent. Especially on a large scale, the swirls and flows form complex patterns that are beautiful and appear information-dense. Having a way to replicate that opens up another class of texturing options.

Stephen’s presentation (which he developed as part of his open-source game Space Nerds in Space) and this Junkship dev blog post about texturing planets looks like a good starting point for using a flow map created with curl noise via procedural fluid flow to create the swirling textures.

No doubt there are other methods out there, but I’m mentioning this one to point out a couple of things:

First, the common procedural generation tools (Perlin noise, in this case) aren’t always the best choice. There’s still lots and lots of new techniques that can be discovered.

Second, try combining processes and information (in the technical sense) from other sources: use flow fields and slime mold growth and plate tectonics and earthquake data and traffic noise into your generators.

Third, one reason why I think this looks better is that turbulent textures have an implied history, and that history is extra information. And the appearance of having denser information makes for better-looking, more convincing generation. It gives it another layer of meaning.

[procedural-generation]

The Signal From Tölva - Automating a Pipeline

Unlike Big Robot’s last game (Sir/Ma’am You Are Being Hunted) their new game The Signal From Tölva doesn’t procedurally generate the map. But their pipeline still used generative tools. You can see the result in the textures in the screenshots above.

Many games and films make extensive use of generative methods behind the scenes. Unless you know how something was made, you might not suspect it. Fortunately, Olly Skillman-Wilson has written a blog post explaining the details of the Tölva texturing pipeline.

http://www.big-robot.com/2017/01/24/art-signal-from-tolva-ian-mcque/

[patternv0mit]

Honestly man. Do you really think you wanna be this wicked stone texture?

[meticulac]

[procedural-generation]

Reflectance Modeling by Neural Texture Synthesis 

It’s that time of year again: SIGGRAPH was last week, giving us a ton of new computer graphics research to be astounded by.

Like this research by Miika Aittala, Timo Alia, and Jaakko Lehtinen: using a single image, a neural network can use parametric texture synthesis to model the reflectance of textures.

If you’re not a computer graphics artist, you might be wondering what the big deal is, so here’s what’s going on:

Describing a material to be rendered involves both the color and the way that it reflects the light. There’s a bunch of ways to approach this, but one common way is to break the texture into its albedo, the basic diffuse color; specularity, the degree to which it reflects its surrounding environment; and glossiness, how mirror-like or scattered that reflection is.

Typically, when you try to photograph something to use for a texture, you want to try to get a photo with the lighting as flat as possible. Any reflections or shadows won’t match the lighting you later add, so the photo needs to have little of it as possible. This is difficult, and the work it takes to get a clean diffuse map and the matching reflection data means that it’s often faster to create the textures from scratch or procedurally generate them.

This is difficult, and the work it takes to get a clean diffuse map and the matching reflection data means that it’s often faster to paint the textures from scratch or procedurally generate them.

An image like this one would be terrible for the usual approach, because of the giant hotspot of a reflection in the middle:

But with this research, that actually helps. Feed this single image into a texture synthesis neural network, and it uses the difference in reflection to learn the optical properties of the material. So you can turn that into this:

Which I think is both a clever idea and a pretty convincing result. I hope this is the kind of thing that will feature prominently in future artistic tools, doing the heavy lifting so we can focus on the image-creation.

https://mediatech.aalto.fi/publications/graphics/NeuralSVBRDF/