Source-linked AI summary
A step towards procedural terrain generation with GANs
Christopher Beckham, Christopher Pal
TL;DR
Procedural terrain generation has relied on handcrafted algorithms, while learning to generate terrain from data remains a goal. This paper uses GANs with NASA imagery to generate heightmaps and corresponding textures; the authors describe the work as a reasonable first step, while noting instability and occasional artifacts.
Problem
Terrain generation for games has traditionally used handcrafted algorithms, motivating a first step toward learning terrain synthesis from data.
Method
A two-stage GAN pipeline learns from NASA imagery, generating heightmaps from random samples and translating them into textures.
Results
The DCGAN produced somewhat faithful 512px heightmaps, and the pix2pix GAN generated textures that roughly matched their corresponding heightmaps.
Takeaways & Limitations
The authors describe the work as a reasonable first step toward procedural terrain generation based on real-world data.
Takeaways & Limitations
Training stability issues and occasional small-scale artifacts constrain the generated heightmaps.
Abstract
from arXiv · showhide
Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA.
1. Introduction
Procedural generation adds unpredictability to games, while existing terrain methods trade fast but simple noise-based results against more labor-intensive handcrafted control. The paper proposes GAN-based learning from NASA maps, with separate stages for generating heightmaps and translating them into textures.
- 1. Introduction: Procedural generation creates game content algorithmically to increase replay value through unpredictability, while handcrafted content is generally higher quality but costs more labor.Minecraft is given as an example of a game built around procedurally generated terrain.
- 1. Introduction: Perlin noise and diamond square are fast but produce simple terrain, while tools such as L3DT offer more sophisticated user control through designed algorithms.The paper motivates learning terrain-generation algorithms with generative networks rather than writing them manually.
- 1. Introduction: The proposed two-stage GAN pipeline generates heightmaps from random samples and infers corresponding textures using high-resolution NASA Visible Earth imagery.It synthesizes 512px maps from random crops of 21,600px × 10,800px source images.
- 1.1. Formulation: The formulation first maps prior samples to heightmaps, then uses a conditional pix2pix GAN to translate heightmaps into textures.The texture discriminator evaluates real heightmap-texture pairs against real-heightmap/generated-texture pairs; the formulation also includes pixel-wise reconstruction loss to discourage mode dropping.
2. Experiments and Results
The DCGAN produces 512px heightmaps described as somewhat faithful to the source images, though training instability and occasional small-scale artifacts remain. The pix2pix stage produces textures that roughly match their heightmaps, with white regions noted as a possible side effect of training the two GANs separately.
- 2. Experiments and Results: The DCGAN generated somewhat faithful 512px heightmaps, despite training-stability issues and occasional small-scale artifacts.The authors suggest deeper architectures, skip connections, or slight Gaussian blur as possible ways to address issues or smooth artifacts.
- 2. Experiments and Results: Generated textures roughly match their corresponding heightmaps, while some white texture regions appear to result from training the DCGAN and pix2pix GANs separately.The paper notes different textures in relatively higher-elevation regions.
3. Conclusion
The authors characterize the work as a reasonable first step toward procedural terrain generation from real-world data. They suggest joint GAN training as a next step and describe possible extensions to terrain metadata and other textured 3D content.
- 3. Conclusion: The paper presents its approach as a reasonable first step toward procedural terrain generation based on real-world data.Jointly training the DCGAN and pix2pix GAN is identified as the most obvious next step.
- 3. Conclusion: A segmentation pipeline could label terrain regions as biomes, providing metadata that a renderer could use to populate areas with corresponding details.The authors call this kind of layer a splatmap in computer graphics.
- 3. Conclusion: The two-stage framework is proposed as applicable beyond terrain, including synthesizing 3D meshes and then texturing them.The authors suggest such applications could support richer entertainment experiences and content producers such as 3D artists.
5. Supplementary material
The supplementary material describes dataset preparation, points to architecture diagrams in the source archive, and reports training choices. The dataset is filtered and narrowed to desert-biome pairs before model training.
- 5.1. Dataset: The dataset is built by sliding a 512px window over paired Earth maps, excluding heightmaps that are over 90% black, then selecting texture pairs closest to a desert reference.The authors use Euclidean distance to select the top M pairs and limit the final collection to the biome of interest.
- 5.2. Architectures: Precise GAN architecture diagrams are available in the source archive.The supplementary material says these details were omitted from the paper body because of page restrictions.
- 5.3. Training: Both GANs use RMSProp with initial learning rates of 1e−4, and the authors use LSGAN because it made training more stable.The LSGAN setup uses binary cross-entropy losses and linear discriminator output activations.