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Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Zexin Hu, Kun Hu, Clinton Mo, Lei Pan, Zhiyong Wang

arXiv:2308.16725v1cs.CVcs.AIcs.MM

TL;DR

Sketch-guided terrain generation must balance structural user control with realistic fine-grained climatic features, while diffusion's stochasticity complicates effective guidance. TDN uses multi-level denoising with terrain and sketch latent spaces to generate terrain from user sketches, and the paper reports state-of-the-art performance.

  • Problem

    Terrain generation must combine realistic fine-grained climatic features with fidelity to structural sketches, while diffusion's stochasticity makes effective user control challenging.

    Source: Introduction · p. 1

  • Method

    TDN uses multi-level denoising with terrain and sketch latent spaces to generate terrain guided by sketches of rivers, ridges, basins, and peaks.

    Source: Introduction · p. 2, Controllable Terrain Synthesis · p. 3

  • Results

    Experiments on a new dataset constructed from NASA Topology Images demonstrate high-quality, realistic terrain synthesis with flexible controls and state-of-the-art performance.

    Source: Introduction · p. 2, Conclusion · p. 8

  • Takeaways & Limitations

    Directly integrated sketch guidance affords users conformity to their inputs while TDN generates climate-aware terrain.

    Source: Conclusion · p. 8

    Generation quality depends heavily on training-data scale, and covering more terrain types can sacrifice fine-grained detail.

    Source: Limitations · p. 7

Abstract

from arXiv · show

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available.

Introduction

Sketch-based terrain generation must balance user control with realistic, geologically precise fine-grained features. TDN addresses this with multi-level, sketch-guided diffusion and reports high-quality realistic synthesis with flexible controls.

Introduction

  • Terrain generation must represent climatic phenomena such as erosion and weathering while maintaining fidelity to user-provided structural guidance.The paper identifies this balance as a challenge for controllable terrain generation.Source: Introduction · p. 1
  • TDN replaces monolithic denoising with structural, intermediate, and fine-grained synthesizers that directly use sketch guidance.Different denoiser weights focus on distinct terrain-pattern scales; terrain and sketch latent spaces use pretrained autoencoders.Source: Introduction · p. 2
  • Experiments on a new dataset constructed from NASA Topology Images demonstrate high-quality, realistic terrain synthesis with flexible user controls.The paper identifies the proposed approach as state of the art.Source: Introduction · p. 2

Related Work

Prior terrain-generation approaches trade off user control, realism, and computational cost in different ways. The related work positions terrain-specific diffusion as a way to combine sketch guidance with diverse, climate-aware terrain generation.

Related Work

  • Procedural methods offer computational efficiency, but user control is limited and generated terrain can lack erosion and weathering.Constrained fractals and parameter adjustments add control, yet procedural terrains may still appear pristine.Source: Procedural Terrain Generation · p. 2

Simulation-based Terrain Generation

  • Simulation-based methods model processes such as erosion and tectonics, but extensive scenarios can be computationally expensive and user control remains limited.These methods can produce more realistic terrain features through simulated geomorphological processes.Source: Simulation-based Terrain Generation · p. 2

Example-based Terrain Generation

  • Example-based methods provide intuitive sketch or image guidance, but increased control can trade off against realism and geological correctness.Examples include blending real terrain patches with user-defined sketches and generating terrain from existing samples.Source: Example-based Terrain Generation · p. 2

Deep-Learning based Terrain Generation

  • GAN-based terrain systems face limits in fine-grained user control and climate-specific generation, while diffusion offers diversity but complicates effective guidance.TDN is presented as integrating multi-level guidance to generate climate-aware terrain aligned with sketches, without requiring multiple specialized datasets.Source: Deep-Learning based Terrain Generation · p. 3

Methodology

TDN combines terrain and sketch autoencoders with sketch-conditioned diffusion to generate terrain at multiple geological levels. Its synthesizers specialize in structural, intermediate, and fine-grained terrain patterns.

Methodology

  • Terrain and sketch autoencoders feed lower-dimensional representations into multi-level terrain synthesisers, reducing diffusion computation while supporting sketch-guided generation.The autoencoders compress terrain and sketch inputs; the diffusion process uses the resulting latent representations.Source: Methodology · p. 3, Terrain and Sketch Autoencoder · pp. 3–4

Controllable Terrain Synthesis

  • Users guide terrain generation with sketches of rivers, ridges, basins, and peaks.The study uses four major control factors, represented as one-channel images.Source: Controllable Terrain Synthesis · p. 3

Terrain and Sketch Autoencoder

  • A U-Net-based terrain autoencoder encodes terrain into a lower-dimensional latent and decodes it back, using perceptual loss to preserve high-level terrain features.Separate sketch encoders and a transformer aggregate sketch guidance into a representation aligned in dimension with the terrain latent.Source: Terrain and Sketch Autoencoder · pp. 3–4

Terrain Diffusion

  • Terrain diffusion iteratively adds Gaussian noise to terrain latents, then estimates reverse transitions with a neural-network denoiser.The reverse distribution estimates the previous latent state from the current one.Source: Terrain Diffusion · p. 4

Multi-Level Sketch Guidance Integration

  • Sketch guidance is concatenated with the noisy terrain latent at every denoising step, while skip connections help keep generated terrain aligned with the sketch.The guidance remains constant across steps, and its latent dimension matches that of the terrain representation.Source: Multi-Level Sketch Guidance Integration · p. 4

Multi-Level Synthesisers

  • Three synthesizers specialize in structural patterns, intermediate coarse details, and fine-grained details such as climatic factors.The structural synthesizer targets the lowest-resolution latent; the intermediate synthesizer balances structural and fine-grained patterns.Source: Multi-Level Synthesisers · pp. 4–5
  • The synthesizers use distinct latent spaces and are trained separately, with sketch guidance encoded to the terrain latent dimension for direct integration.Their varied weights tailor guidance to different stages of diffusion.Source: Multi-Level Synthesisers · p. 5

Experiments & Discussions

The study uses terrain samples collected from NASA, with extracted elevation maps and sketches representing four terrain features.

  • The dataset contains 10,446 training samples and 2,611 test samples.Source: Dataset · p. 5
  • Each extracted elevation map is 144 × 144, and the images have a 1:6400 meter scale.Source: Dataset · p. 5
  • Sketches represent basins, peaks, rivers, and ridges, extracted using Pysheds DEM conditioning techniques.The stated techniques include pit filling and flow direction determination.Source: Dataset · p. 5

Implementation Details

TDN uses cosine-scheduled latent diffusion with U-Net-like synthesisers and performs iterative denoising at inference.

  • A cosine scheduler controls gradual noise addition to the terrain latent representation.Source: Implementation Details · p. 5
  • Each synthesiser uses a U-Net-like architecture with three downsampling blocks, three upsampling blocks, and an eight-head self-attention middle block.Source: Implementation Details · p. 5
  • The model has approximately 1.216 billion parameters; training uses a 1.0e-05 learning rate and batch size 6.Source: Implementation Details · p. 5
  • At inference, TDN takes user sketches and iterates for 36 steps to produce a noise-free terrain latent representation.Experiments used an Nvidia RTX 3090 GPU.Source: Implementation Details · p. 5

Overall Performance

TDN surpasses the compared terrain-generation techniques in quantitative evaluations and produces terrains that preserve natural fidelity while adhering to input sketches. The study evaluates GAN- and diffusion-based methods using FID and MSE, with qualitative comparisons to GAN and Stable Diffusion.

Ablation Study

The ablations examine TDN’s multi-level denoising, intermediate synthesiser, sketch autoencoders, and direct sketch guidance. They report quantitative and qualitative evidence about these mechanisms’ effects on performance and sketch adherence.

Ablation Study

  • The ablation study assesses individual TDN mechanisms using quantitative results in Table 2 and qualitative results in Figure 4.Source: Ablation Study · p. 6

TDN w/o Multi-Level Synthesizers

  • Without multi-level synthesisers, FID and MSE drop 13.18% and 16.55%, respectively, compared with TDN’s multi-level scheme.The multi-level scheme also offers flexible guidance weighting for adapting to user demands.Source: TDN w/o Multi-Level Synthesizers · p. 6

TDN w/o Intermediate-Level Synthesizers

TDN w/o Sketch Autoencoders (AEs)

TDN w/o Direct Sketch Guidance

  • Direct sketch integration improves terrain–sketch consistency compared with cross-attention, which adversely affects terrain generation in Stable Diffusion and ControlNet.Source: TDN w/o Direct Sketch Guidance · p. 7

Out-of-Domain Generation

On human-provided sketches, TDN handles conflicting terrain controls and climatic conditioning while producing terrain described as faithful to the inputs and realistic. The examples contrast this with a GAN that favors peaks over other sketch inputs.

Out-of-Domain Generation

Limitations

TDN’s quality depends on training-data scale, and broader terrain coverage can come at the cost of granular detail. The paper also identifies computational expense and fixed sketch-based controls as constraints.

Limitations

  • Broader terrain coverage can sacrifice granular detail because the diffusion model learns and denoises within the training data’s distribution.The authors note that obtaining diverse, high-resolution terrain data is challenging, while combining data from multiple sources could build a larger database at substantial training cost.Source: Limitations · p. 7
  • Independent training of the terrain and sketch autoencoders makes overall training computationally expensive; 144 × 144 inference takes around 11 seconds for 36 steps.Source: Limitations · p. 7
  • Current control is limited to a fixed number of user-provided sketches, while customized sketch categories and text guidance remain future directions.Source: Limitations · p. 7

Conclusion

The conclusion presents TDN as a sketch-guided diffusion method with three denoising synthesizers and pre-trained terrain and sketch latent spaces. Experiments on the newly collected dataset are reported to demonstrate state-of-the-art performance.

Conclusion

  • TDN uses three synthesizers in a multi-level denoising scheme to formulate structural and fine-grained terrain characteristics, including climatic patterns.Source: Conclusion · pp. 7–8
  • Experiments on the newly collected dataset are reported to demonstrate TDN’s state-of-the-art performance.Source: Conclusion · p. 8
  • Pre-trained autoencoders provide terrain and sketch latent spaces aimed at user-sketch consistency.Source: Conclusion · p. 8
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