In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush
This addresses the domain-specific challenge of automating 3D game map art creation for game developers and artists, offering a novel tool to enhance production efficiency and artistic flexibility.
The paper tackles the problem of high-resolution texture manipulation in complex 3D game maps by introducing a Smart Brush that uses generative AI to assist artists in seamless map editing, with results showing that their GAN-based variant produces sharper and more detailed outputs compared to state-of-the-art models.
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.