眨眼之间：使用生成式AI智能画笔即时编辑游戏地图
In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush
AI总结：
针对3A游戏地图高分辨率纹理编辑难题，提出基于GAN和扩散模型的智能画笔，实现上下文感知的即时地图修改，兼顾自动化与艺术控制，且GAN变体输出更清晰。
摘要：
随着视频游戏复杂性的稳步提升，游戏内容的自动生成已引起广泛关注。然而，由于3D游戏地图艺术创作独特的复杂性和领域特定挑战，该任务至今仍未被充分探索。尽管近期工作已涉及复古风格关卡生成和程序化地形创建等相关主题，但这些工作主要聚焦于更简单的数据分布。据我们所知，我们是首个在现代AI技术应用于复杂、高度精细的AAA级3D游戏环境中进行高分辨率纹理操作方面进行演示的团队。我们引入了一种新颖的用于地图编辑的智能画笔，旨在帮助艺术家以最小努力无缝修改游戏地图的选定区域。通过利用生成对抗网络和扩散模型，我们提出了该画笔的两种变体，以实现高效且上下文感知的生成。我们的混合工作流旨在增强艺术灵活性和生产效率，使得无需手动重新处理每个细节即可完善环境，从而有助于弥合游戏开发中自动化与创造性控制之间的鸿沟。将我们的两种方法与多个最先进模型的改编版本进行比较评估，结果表明，基于GAN的画笔在保留图像上下文的同时产生了最清晰、最详细的输出，而被评估的最先进模型则倾向于产生更模糊的结果，并在保持上下文一致性方面表现出困难。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
