用叙事理论指导AI创作漫画，让人类与AI协作更顺畅。
Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity
- 将漫画创作规律转化为可执行的系统规则，支持分步生成
- 提升画面布局、情节张力和转场效果的连贯性
- 适合想高效创作漫画的创作者或内容团队
本研究提出一种受叙事理论启发的视觉叙事生成系统，将人类创作中积累的漫画创作惯例（comic authoring idioms）融入生成模型与语言模型，以增强漫画创作过程。该系统通过人机协作方式支持部分内容生成，利用源自真实图像序列的创作原则作为指导，构建多层结构实现叙事元素的逐步决策，涵盖分镜构图、情节张力变化与分镜转换等关键方面。核心贡献包括：将机器学习模型整合进人机协同漫画生成流程；将抽象叙事理论落地为可驱动的AI创作机制；提供可定制的叙事导向图像序列生成工具。实验表明该方法显著提升了生成图像序列的叙事质量，并有效激发了人类在AI辅助创作中的创造力。代码已开源：https://github.com/RimiChen/Collaborative_Comic_Generation。
原文摘要 · Abstract (English)
This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.
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