The Uncanny Valley of Procedural Generation

As a developer of procedural worlds, what worries me most is not failing spectacularly at my goals. My biggest fear is to produce something that looks believable, but still is somehow off. Even a seemingly perfect world could be rejected by your subconsciousness. You may not be able to put it into words, or point your finger at it, but you feel there is something wrong. 

To make matters even worse, it seems we can be collectively hypnotized into liking something just because it is a new way of doing things. Soon the novelty wears off and we realize the Emperor was naked all the time. 

We want to believe things look better that they do. For instance we know 3D graphics are a developing field so we are ready to forgive a lot, until something better appears and sets a new standard.

Remember this beauty?

This is Peter Gabriel's "Kiss That Frog". You can see the video here. It won a MTV award for special effects in 1994. I remember loving this video. It is hard to watch now.

This is not specific of procedural techniques. Humans are equally able to generate uncanny, ugly things. The problem is proceduralism makes it a lot easier. The world is not crafted by hand, any aberration in the algorithms will be mindlessly multiplied. It also depends on the degree of realism you want to achieve. If you are trying to fake nature, odds are your creation is some form of monstrosity that will not stand the pass of time.

I often wonder whether any sort of synthetic reality is doomed in the long term. At this point I don't know for sure, but I have two simple ideas to guide me across this maze:

1. Global rather than Local

Procedural methods can be divided into two large families, global methods and local methods. In local methods content generated for a given point in space does not depend on the content of neighbor points.

The Perlin noise function, for instance, can be evaluated locally. This means the output of the function depends only on the coordinates of the point, plus some constants. It also means many points can be evaluated in parallel. Since they are isolated from their neighbors, you do not need to compute any neighbors before evaluating a single point.

They are blindingly fast, but it comes at a cost: They do not have a soul. They do not produce any information. All what you see comes from a small seed of values and the specific ways these values are churned and shaken by these clever algorithms.

Because of their speed and fairly good results, they can be used in many subtle places, but they should not be the backbone of your world. Our minds are very good a discovering redundancy. All these methods are like a kaleidoscope effect, they can trick you for a moment but soon you start seeing the mirrors. And once you have seen them, the magic is gone for good.

Here is your typical multifractal Perlin terrain:

Just don't do it. We all know it is not real.

Another popular local method is L-Systems. This is when they are in their vanilla form, as context free grammars where symbols are replaced with no awareness of their surroundings. If used to produce trees, you soon have branches that intersect each other, or that go into illogical directions.

Here is one gem that illustrates this point:

Global methods, on the other hand, are closer to simulations. The value of a single point may depend on the values of very distant points. Imagine a fluvial erosion filter. A point at the base of a mountain may be largely influenced by a large streak of points uphill.

Global methods ere effective because they have cause-effect relationships built into them. This makes all the difference. It brings entropy into the world, it gives it time and history.

Here is an example showing some fluvial erosion. The patterns you see here are far more believable:

In the same fashion, most successful tree and plant generators use some sort of simulation or at least global constraints. For my trees I chose a global method that grows branches in full awareness of each other. It is a very simple algorithm and still beats the results you get from vanilla L-Systems.

The problem with simulations is they are costly to evaluate. There is information moving around so they are harder to compute in parallel. 

Consider this example. The location, shape and strength of a river will be determined by a water source many kilometers away. A visitor to the virtual world may encounter the river long before than the source, but still the source and everything between must be accounted for. For worlds that are generated on demand as the viewer moves this may be too much to handle.

Even then, you should always consider using a global method. It may make your solution complex and slower, but you will have something to show.

2. Steal from Mother (Nature)

Nature has already spent a lot of time and energy producing the patterns we accept as real. We already use them to texture 3D models, there is no reason why we couldn't go beyond that.

Here is a very interesting approach to terrain synthesis. It combines elevation samples from real sites from Earth and stitches them into new ways. This results in fairly believable scenes that can cover huge spaces without any apparent repetition.

In this case they used some samples taken from the Grand Canyon.

A similar technique can be used for smaller terrain features like rocks, cliffs and small boulders. It is possible to have a set of volumetric natural textures and map them over larger terrain features. 

The biggest issue is how to mask the repetition, but this can be done using Wang tiles. I have reworked many of my core functions to be like this and I like them better than my previous functions. I will be posting some results soon.

To sum it up, I think entropy matters.