mlsanityData to 14 Oct 2026

TerraFusion: Joint Generation of Terrain Geometry and Texture Using Latent Diffusion Models

arXiv 2505.04050 ↗First posted 7 May 2025cs.GR cs.CVcited by 1

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

3D terrain models are essential in fields such as video game development and film production. Since surface color often correlates with terrain geometry, capturing this relationship is crucial to achieving realism. However, most existing methods generate either a heightmap or a texture, without sufficiently accounting for the inherent correlation. In this paper, we propose a method that jointly generates terrain heightmaps and textures using a latent diffusion model. First, we train the model in an unsupervised manner to randomly generate paired heightmaps and textures. Then, we perform supervised learning of an external adapter to enable user control via hand-drawn sketches. Experiments show that our approach allows intuitive terrain generation while preserving the correlation between heightmaps and textures.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 13 of its 30 references that are in the map. First posted on arXiv 7 May 2025.

Builds on

13 of 30 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  5. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  6. 2023Consistency Modelscited by 1,822
  7. 2022The Role of ImageNet Classes in Fr'echet Inception Distancecited by 213
  8. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  9. 2019PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfercited by 66
  10. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  11. 2025Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Modelscited by 25
  12. 2025MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Datacited by 7
1 more
  1. 2021Seamless Satellite-image Synthesiscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2023Single Mesh Diffusion Models with Field Latents for Texture Generation
  2. 2025InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation
  3. 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
  4. 2024Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
  5. 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
  6. 20233DGen: Triplane Latent Diffusion for Textured Mesh Generation
  7. 2024LT3SD: Latent Trees for 3D Scene Diffusion
  8. 2023TEXTure: Text-Guided Texturing of 3D Shapes