mlsanityData to 14 Oct 2026

In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush

arXiv 2503.19793 ↗First posted 25 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

With video games steadily increasing in complexity, automated generation of game content has found widespread interest. However, the task of 3D gaming map art creation remains underexplored to date due to its unique complexity and domain-specific challenges. While recent works have addressed related topics such as retro-style level generation and procedural terrain creation, these works primarily focus on simpler data distributions. To the best of our knowledge, we are the first to demonstrate the application of modern AI techniques for high-resolution texture manipulation in complex, highly detailed AAA 3D game environments. We introduce a novel Smart Brush for map editing, designed to assist artists in seamlessly modifying selected areas of a game map with minimal effort. By leveraging generative adversarial networks and diffusion models we propose two variants of the brush that enable efficient and context-aware generation. Our hybrid workflow aims to enhance both artistic flexibility and production efficiency, enabling the refinement of environments without manually reworking every detail, thus helping to bridge the gap between automation and creative control in game development. A comparative evaluation of our two methods with adapted versions of several state-of-the art models shows that our GAN-based brush produces the sharpest and most detailed outputs while preserving image context while the evaluated state-of-the-art models tend towards blurrier results and exhibit difficulties in maintaining contextual consistency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 68 references that are in the map. First posted on arXiv 25 Mar 2025.

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28 of 68 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
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  11. 2018Image Transformercited by 772
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16 more
  1. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  2. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  3. 2020Focal Frequency Loss for Image Reconstruction and Synthesiscited by 206
  4. 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 200
  5. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  6. 2016Mode Regularized Generative Adversarial Networkscited by 123
  7. 2019Image inpainting: A reviewcited by 89
  8. 2024Distilling Diffusion Models into Conditional GANscited by 78
  9. 2024Improving Diffusion-Based Image Synthesis with Context Predictioncited by 32
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  12. 2023The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddingscited by 14
  13. 2022Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learningcited by 1
  14. 2023GAN-Based Content Generation of Maps for Strategy Gamescited by 1
  15. 2023Reconstructing Existing Levels through Level Inpaintingcited by 1
  16. 2024Enhancing Diffusion Models for High-Quality Image Generationcited by 1

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  1. 2024Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches
  2. 2024Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
  3. 2025Style Transfer: A Decade Survey
  4. 2025Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers
  5. 2025WorldGen: From Text to Traversable and Interactive 3D Worlds
  6. 2025Model as a Game: On Numerical and Spatial Consistency for Generative Games
  7. 2026Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes
  8. 2025TerraFusion: Joint Generation of Terrain Geometry and Texture Using Latent Diffusion Models