that’s pricey to me (and to many others) as a result of it has, in a manner, watched me develop from an elementary faculty scholar, all the way in which to a (soon-to-be!) school graduate. An plain a part of the sport’s appeal is its infinite replayability derived from its world technology. In present editions of the sport, Minecraft makes use of a wide range of noise capabilities in conjunction to procedurally generate [1] its worlds within the type of chunks, that’s, blocks, in a manner that tends to (roughly) kind ‘pure’ wanting terrain, offering a lot of the immersion for the sport.
My aim with this challenge was to see if I might transfer past hard-coded noise and as a substitute educate a mannequin to ‘dream’ in voxels. By leveraging current developments in Vector Quantized Variational Autoencoders (VQ-VAE) and Transformers, I constructed a pipeline to generate 3D world slices that seize the structural essence of the sport’s landscapes. As a concrete output, I wished the flexibility to generate chunks (organized in a grid) that regarded like Minecraft’s terrain.
As a aspect notice, this isn’t a wholly novel thought, specifically, ChunkGAN [2] gives an alternate method to handle the identical aim.
The Problem of 3D Generative Modeling
In a video [3] from January 2026, Computerphile featured Lewis Stuart that highlighted the primary points with 3D technology and I might encourage readers to present it a watch nonetheless, to summarize the important thing factors, 3D technology is difficult as a result of good 3D datasets are onerous to search out or just don’t exist and including a dimension of freedom makes issues a lot more durable (take into account the traditional Three-body problem [4]). It needs to be famous that the video explicitly addresses diffusion fashions (which requires labelled information) although most of the considerations could be ported over to the overall thought of 3D technology. One other concern is solely scale; a picture ( pixels) would virtually definitely be thought of low-resolution by fashionable requirements however a 3D mannequin on the similar constancy would require voxels. Extra factors instantly implies increased compute necessities and may rapidly make such duties infeasible.
To beat the 3D information shortage talked about by Stuart, I turned to Minecraft, which, in my view, is the perfect supply of voxel information out there for terrain technology. Through the use of a script to teleport by means of a pre-generated world, I pressured the sport engine to load and render 1000’s of distinctive chunks. Utilizing a separate extraction script, I pulled these chunks instantly from the sport’s area recordsdata. This gave me a dataset with excessive semantic consistency; in contrast to a group of random 3D objects, these chunks signify a steady, flowing panorama the place the ‘logic’ of the terrain (how a river mattress dips or how a mountain peaks) is preserved over chunk boundaries.
To bridge the hole between the complexity of 3D voxels and the constraints of recent {hardware}, I couldn’t merely feed uncooked chunks right into a mannequin and hope for the perfect. I wanted a strategy to condense the ‘noise’ of thousands and thousands of blocks right into a significant, compressed language. This lead me to the center of the challenge: a two-stage generative pipeline that first learns to ‘tokenize’ 3D house, after which learns to ‘communicate’ it.
Information Preprocessing
A key but non-obvious statement is that a good portion of Minecraft’s chunks are stuffed with ‘air’ blocks. It’s a non-trivial statement principally as a result of air isn’t technically a block, you’ll be able to’t place it or take away it as you’ll be able to with each different block within the recreation however quite, it’s the non-existence of a block at that time. In fashionable Minecraft, a lot of the vertical span is air and as such, as a substitute of contemplating full peak ranges, I restricted it to . These extra aware of Minecraft’s world technology would know that blocks have damaging -values, all the way in which to and at this level, I need to apologize as a result of once I carried out this structure, this had solely slipped my thoughts. The mannequin I current on this article would work simply as effectively when you thought of a bigger vertical span however as a result of my unlucky oversight, the outcomes that I current will likely be from a restricted span of blocks.
On the notice of proscribing blocks, chunks have lots of blocks that don’t present up fairly often and don’t contribute to the overall form of the terrain however vital to keep up immersion for the participant. A minimum of for this challenge, I select to limit blocks to the highest 30 blocks that made up chunks by frequency.
Pruning the vocabulary, so to talk, is helpful however solely half the battle. As said earlier than, as a result of Minecraft worlds are primarily composed of ‘air’ and ‘stone,’ the dataset suffers from some fairly excessive class imbalance. To stop the mannequin from taking the ‘path of least resistance,’ that’s, merely predicting empty house to realize low loss, I carried out a Weighted Cross-Entropy loss. By scaling the loss primarily based on the inverse log-frequency of every block, I pressured the VQ-VAE to prioritize the structural ‘minorities’ like grass, water, and snow.
In plain phrases: the rarer a block sort is within the dataset, the extra closely the mannequin is penalized for failing to foretell it, pushing the community to deal with a patch of snow or a river mattress as simply as vital because the huge expanses of stone and air that dominate most chunks.
Structure Overview
This mermaid sequenceDiagram [6] gives a chook’s eye view of the structure.
Uncooked Voxel Downside and Tokenizing 3D Area
A naive method to constructing such an structure would contain studying and constructing chunks block by block. There’s a myriad of the reason why this is able to be unideal however an important downside is that it may possibly grow to be computationally infeasible in a short time with out actually offering semantic construction. Think about assembling a LEGO set with 1000’s of bricks. Whereas doable, it could be manner too gradual and it wouldn’t actually have any structural integrity: items which can be adjoining horizontally wouldn’t be linked and also you’d primarily be constructing a set of disjoint towers. The best way LEGO addresses that is by having bigger blocks, like the enduring brick, that take over house that will usually require a number of items. As such, you refill house quicker and there’s extra structural integrity.
For the system, codewords are the LEGO bricks. Utilizing a VQ-VAE (Vector Quantized Variational AutoEncoder), the aim is to construct a codebook, that’s, a set of structural signatures that it may possibly use to reconstruct full chunks. Consider buildings like a flat part of grass or a blob of diorite. In my implementation, I allowed a codebook with distinctive codes.
To implement this, I used 3D Convolutions. Whereas 2D convolutions are the bread and butter of picture processing, 3D convolutions enable the mannequin to be taught kernels that slide throughout the X, Y, and Z axes concurrently. That is very important for Minecraft, the place the connection between a block and the one beneath it (gravity/help) is simply as vital as its relationship to the one beside it.
Additional Particulars
Probably the most crucial part of this stage is the `VectorQuantizer`. This layer sits on the ‘bottleneck’ of the community, forcing steady neural indicators to snap to a set ‘vocabulary’ of 512 realized 3D shapes.
Certainly one of my largest hurdles in VQ-VAE coaching is ‘lifeless’ embeddings, that’s, codewords that the encoder by no means chooses, which successfully waste the mannequin’s capability. To unravel this, I added a strategy to ‘reset’ lifeless codewords. If a codeword’s utilization drops too low, the mannequin forcefully re-initializes it by ‘stealing’ a vector from the present enter batch:
Brick by Brick
A various assortment of blocks is nice however they don’t imply a lot except they’re put collectively effectively. Due to this fact, to place these codewords to good use, I used a GPT. To be able to make this work, I took the latent grid produced by the VQ-VAE right into a set of tokens, primarily, the 3D world will get flattened right into a 1D language. Then, the GPT sees 8 chunks value of tokens to be taught the spatial grammar, so to talk, of Minecraft to realize the aforementioned semantic consistency.
To realize this, I used Informal Self-Consideration:
Lastly, throughout inference, the mannequin makes use of top-k sampling, together with some temperature to regulate erratic technology creativity within the following technology loop:
By the top of this sequence, the GPT has ‘written’ a structural blueprint 256 tokens lengthy. The subsequent step is to cross these by means of the VQ-VAE decoder to manifest a grid of recognizable Minecraft terrain.
Outcomes
On this render [6], the mannequin efficiently clusters leaf blocks, mimicking the sport’s tree buildings.
On this one [6], the mannequin makes use of snow blocks to cap the stone and grass, reflecting the high-altitude or tundra slices discovered within the coaching information. Moreover, this render reveals that the mannequin realized generate caves.
On this picture [6], the mannequin locations water in a despair and borders it with sand, demonstrating that it has internalized the spatial logic of a shoreline, quite than scattering water blocks arbitrarily throughout the floor.
Maybe essentially the most spectacular result’s the interior construction of the chunks. As a result of the implementation used 3D convolutions and a weighted loss perform, the mannequin really generates subterranean options like contiguous caves, overhangs, and cliffs.
Whereas the outcomes are recognizable, they aren’t excellent clones of Minecraft. The VQ-VAE’s compression is ‘lossy,’ which typically leads to a slight ‘blurring’ of block boundaries or the occasional floating block. Nevertheless, for a mannequin working on a extremely compressed latent house, the flexibility to keep up structural integrity throughout a chunk grid, I imagine, is a big success.
Reflections and Future Work
Whereas the mannequin efficiently ‘goals’ in voxels, there may be important room for enlargement. Future iterations might revisit the complete vertical span of to accommodate the huge jagged peaks and deep ‘cheese’ caves attribute of recent Minecraft variations. Moreover, scaling the codebook past 512 entries would enable the system to tokenize extra complicated, area of interest buildings like villages or desert temples. Maybe most enjoyable is the potential for conditional technology, or ‘biomerizing’ the GPT, which might allow customers to information the architectural course of with particular prompts corresponding to ‘Mountain’ or ‘Ocean,’ turning a random dream right into a directed artistic software.
Thanks for studying! Should you’re within the full implementation or wish to experiment with the weights your self, be happy to take a look at the repository [5].
Citations and Hyperlinks
[1] Minecraft Wiki Editors, World technology (2026), https://minecraft.wiki/w/World_generation
[2] x3voo, ChunkGAN (2024), https://github.com/x3voo/ChunkGAN
[3] Lewis Stuart for Computerphile, Producing 3D Fashions with Diffusion – Computerphile (2026), https://www.youtube.com/watch?v=C1E500opYHA
[4] Wikipedia Editors, Three-body Downside (2026), https://en.wikipedia.org/wiki/Three-body_problem
[5] spaceybread, glowing-robot (2026), https://github.com/spaceybread/glowing-robot/tree/grasp
[6] Picture by creator.
A Notice on the Dataset
All coaching information was generated by the creator utilizing a regionally run occasion of Minecraft Java Version. Chunks have been extracted from procedurally generated world recordsdata utilizing a customized extraction script. No third-party datasets have been used. As the information was generated and extracted by the creator from their very own recreation occasion, no exterior licensing restrictions apply to its use on this analysis context.
