Cutscenes are carefully choreographed cinematic sequences embedded in video games and interactive media, serving as the primary vehicle for narrative delivery, character development, and emotional engagement. Producing cutscenes is inherently complex: it demands seamless coordination across screenwriting, cinematography, character animation, voice acting, and technical direction, often requiring days to weeks of collaborative effort from multidisciplinary teams to produce minutes of polished content. In this work, we present Cutscene Agent, an LLM agent framework for automated end-to-end cutscene generation. The framework makes three contributions: (1)~a Cutscene Toolkit built on the Model Context Protocol (MCP) that establishes \emph{bidirectional} integration between LLM agents and the game engine -- agents not only invoke engine operations but continuously observe real-time scene state, enabling closed-loop generation of editable engine-native cinematic assets; (2)~a multi-agent system where a director agent orchestrates specialist subagents for animation, cinematography, and sound design, augmented by a visual reasoning feedback loop for perception-driven refinement; and (3)~CutsceneBench, a hierarchical evaluation benchmark for cutscene generation. Unlike typical tool-use benchmarks that evaluate short, isolated function calls, cutscene generation requires long-horizon, multi-step orchestration of dozens of interdependent tool invocations with strict ordering constraints -- a capability dimension that existing benchmarks do not cover. We evaluate a range of LLMs on CutsceneBench and analyze their performance across this challenging task.

翻译：过场动画是嵌入在电子游戏与交互媒体中的精心编排的电影化片段，承担着叙事呈现、角色塑造和情感共鸣的核心载体功能。过场动画制作具有固有问题复杂性：它需要编剧、电影摄影、角色动画、配音与导演技术等多环节的无缝协同，通常需要跨学科团队耗费数日至数周的合作努力，才能产出数分钟的精良内容。本文提出Cutscene Agent这一基于大语言智能体框架的自动化端到端过场动画生成方案。该框架贡献如下三方面：(1) 基于模型上下文协议(MCP)构建的过场动画工具包(Cutscene Toolkit)，实现大语言智能体与游戏引擎的 *双向* 集成——智能体不仅能调用引擎操作，还可持续观察实时场景状态，从而实现可编辑引擎原生电影资产的全闭环生成；(2) 多智能体系统，由导演智能体统筹管理动画、摄影与音效设计的专业子智能体，并通过视觉推理反馈循环实现感知驱动的精细化迭代；(3) 面向过场动画生成的分层评估基准CutsceneBench。不同于评估简短孤立函数调用的典型工具调用基准，过场动画生成需要长时效、多步骤的编排，严格约束下协调数十个相互依赖的工具调用——这一能力维度是现有基准尚未覆盖的。我们在CutsceneBench上对多种大语言模型进行评估，分析其在此项挑战性任务中的表现。