用自然语言自动生成游戏界面，提升设计效率与一致性。
GameUIAgent: An LLM-Powered Framework for Automated Game UI Design with Structured Intermediate Representation
- 通过中间表示结构化生成可编辑的Figma设计
- 发现质量天花板效应与渲染评估失真现象
- 适合游戏开发团队快速原型设计与自动化流程
游戏界面设计需在不同稀有度等级间保持视觉一致性，但目前仍以人工为主。我们提出GameUIAgent，一个基于大模型的智能框架，将自然语言描述转化为可编辑的Figma设计，中间通过设计规范JSON表示。六阶段神经符号流水线结合大模型生成、确定性后处理与视觉-语言模型引导的反思控制器（RC），实现迭代自纠错且质量不退步。在110个测试案例、3种大模型与3种界面模板下评估，跨模型分析建立游戏领域失败分类体系（稀有度依赖退化；视觉空洞），并揭示两项关键发现：质量天花板效应（皮尔逊相关r=-0.96，p<0.01）表明RC改进受质量阈值限制——视觉领域类似测试时计算缩放律；渲染-评估保真度原则显示，部分渲染优化反而因放大结构缺陷而降低VLM评价结果。这些成果为游戏生产中大模型视觉生成代理奠定基础。
原文摘要 · Abstract (English)
Game UI design requires consistent visual assets across rarity tiers yet remains a predominantly manual process. We present GameUIAgent, an LLM-powered agentic framework that translates natural language descriptions into editable Figma designs via a Design Spec JSON intermediate representation. A six-stage neuro-symbolic pipeline combines LLM generation, deterministic post-processing, and a Vision-Language Model (VLM)-guided Reflection Controller (RC) for iterative self-correction with guaranteed non-regressive quality. Evaluated across 110 test cases, three LLMs, and three UI templates, cross-model analysis establishes a game-domain failure taxonomy (rarity-dependent degradation; visual emptiness) and uncovers two key empirical findings. A Quality Ceiling Effect (Pearson r=-0.96, p<0.01) suggests that RC improvement is bounded by headroom below a quality threshold -- a visual-domain counterpart to test-time compute scaling laws. A Rendering-Evaluation Fidelity Principle reveals that partial rendering enhancements paradoxically degrade VLM evaluation by amplifying structural defects. Together, these results establish foundational principles for LLM-driven visual generation agents in game production.
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