用AI智能画笔一键修改3D游戏地图,又快又准还保细节。
In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush
- 结合GAN与扩散模型,打造可智能生成纹理的画笔工具。
- 相比现有模型,生成图像更清晰、上下文一致性更强。
- 适合游戏美术师快速迭代复杂场景,提升创作效率。
随着视频游戏复杂度不断提升,自动化游戏内容生成受到广泛关注。然而,由于其独特的复杂性和领域特定挑战,3D游戏地图艺术创作仍鲜有研究。尽管已有工作涉及复古风格关卡生成和程序化地形创建,但主要聚焦于简单数据分布。据我们所知,这是首个将现代AI技术应用于高分辨率纹理操作的成果,针对复杂、高度细致的AAA级3D游戏环境。本文提出一种新型智能画笔,帮助艺术家以最少操作无缝修改地图指定区域。通过融合生成对抗网络(GAN)与扩散模型,设计了两种变体,实现高效且上下文感知的生成。混合工作流旨在提升艺术灵活性与生产效率,使环境精修无需逐项重制细节,从而弥合自动化与创作控制之间的差距。对比评估显示,基于GAN的画笔在保持图像上下文的同时,生成结果最锐利、细节最丰富;而对比的先进模型则趋向模糊,并难以维持上下文一致性。
原文摘要 · Abstract (English)
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.
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