SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution

📄 arXiv: 2608.17468v1 📥 PDF

作者: Maolin Ran, Xiaoyang Lu, Jiaqi Liu, Jian Wang, Weiwen Liu, Jianghao Lin, Yong Yu, Weinan Zhang

分类: cs.AI

发布日期: 2026-08-18

备注: 11 pages, 9 figures, 4 tables. Dataset available at https://github.com/creDreams/PROSE

🔗 代码/项目: GITHUB

💡 一句话要点

提出SAGE框架以解决自动化故事板制作中的知识获取与演化问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 故事板制作 知识演化 自动化创作 归因引导 影视制作 大型语言模型 规则学习 虚拟现实

📋 核心要点

  1. 现有的故事板制作方法依赖于导演的隐性知识，导致知识获取和注入的效率低下。
  2. SAGE框架通过对比训练剧本与专家故事板，自动学习和演化导演知识，解决了知识获取和反馈的问题。
  3. 在18个测试剧集上，SAGE得分77.8，且在实际应用中减少了83%的创作时间，显示出显著的效果提升。

📝 摘要（中文）

故事板将剧本转化为视觉镜头计划，然而专业的故事板制作依赖于导演的隐性经验，成为工业瓶颈。虽然大型语言模型可以自动化这一过程，但在知识获取、知识精炼和知识注入方面面临挑战。本文提出SAGE（基于归因引导的技能演化框架），通过对比训练剧本与专家故事板，学习、归因、演化和路由导演知识。SAGE在18个测试剧集上得分77.8，超过专业导演的77.1，并在实际应用中显著减少了创作时间。我们还发布了PROSE数据集，首次将剧本与专业导演的故事板配对。

🔬 方法详解

问题定义：本文旨在解决自动化故事板制作中的知识获取、精炼和注入问题。现有方法依赖于隐性知识，导致知识难以有效利用和反馈。

核心思路：SAGE框架通过对比训练剧本与专家故事板，自动学习导演知识并进行演化，确保知识的适应性和可用性。

技术框架：SAGE的整体架构包括知识学习模块、归因记录模块和知识演化模块。模型在生成过程中记录每个叙事组采用的规则，并结合反馈进行针对性更新。

关键创新：SAGE的主要创新在于其归因引导的演化机制，使得规则的演化与具体场景相适应，避免了传统方法中的知识注入问题。

关键设计：SAGE采用了基于规则的学习方法，结合局部反馈进行规则更新，确保每个叙事组仅检索适合其情境的规则，优化了知识的使用效率。

🖼️ 关键图片

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📊 实验亮点

在18个测试剧集上，SAGE得分77.8，超过专业导演的77.1，显示出其在自动化故事板制作中的有效性。此外，SAGE在实际应用中减少了83%的创作时间，表明其显著提升了生产效率。

🎯 应用场景

SAGE框架具有广泛的应用潜力，尤其在影视制作、游戏设计和虚拟现实等领域。通过自动化故事板制作，能够显著提高创作效率，降低人力成本，并为创作者提供更高质量的视觉叙事支持。未来，SAGE可能在其他需要知识演化和自动化决策的领域中发挥重要作用。

📄 摘要（原文）

Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial expertise and remains an industrial bottleneck. Large language models can automate this step, but methods for supplying directing knowledge face three challenges: (1) Knowledge acquisition: the craft remains implicit in exemplars or must be written manually. (2) Knowledge refinement: authored knowledge is not evaluated against execution outcomes, and opaque generation prevents feedback attribution to the knowledge behind each decision. (3) Knowledge injection: injecting all knowledge exceeds usable context, while manual selection for every narrative group does not scale. We present SAGE (Skill with Attribution-Guided Evolution), a deployed framework that learns, attributes, evolves, and routes directing knowledge from expert demonstrations. SAGE derives rules that are independent of episode content by contrasting each training screenplay with its expert storyboard. During generation, the model records each narrative group's adopted rules. Combining these records with localized feedback enables targeted updates to individual rules. Evolved rules form scenario packages with a routing index, so each group retrieves only a bounded set appropriate to its situation without expert intervention. On 18 test episodes across three genres, SAGE scored 77.8 on a rubric validated by experts, versus 77.1 for professional directors. Deployed for 14 days on Virtual Film Studio, SAGE produced 1,344 narrative group outputs; 87.2 percent were accepted without substantive edits, and the production team recorded over 83 percent less authoring time per episode. We release PROSE, the first public dataset pairing screenplays with storyboards by professional directors across 68 episodes: https://github.com/creDreams/PROSE.