用图像激发角色创意，让故事更生动有趣。
A Character-Centric Creative Story Generation via Imagination
- 通过文本生成图像，让角色和场景更具体
- 多作者模式生成角色描述，提升人物深度
- 支持人机互动创作，适合创意写作与教育
创意故事生成一直是自然语言处理的研究目标。尽管现有方法致力于生成长篇连贯的故事，但在多样性与角色深度方面仍远不及人类水平。为此，我们提出一种新的以角色为中心的创意故事生成框架CCI（Character-centric Creative story generation via Imagination）。CCI包含两个模块：图像引导想象（IG）和多作者模型（MW）。在IG模块中，利用文本到图像模型生成角色、背景和核心情节的视觉表征，比纯文本方式更具新颖性和具体性。MW模块基于这些元素生成多个角色描述候选，并选择最优者融入故事，从而增强叙事的丰富性与深度。我们通过统计分析、人工评估及大模型评估对比了CCI与基线模型生成的故事。结果表明，IG和MW模块显著提升了故事的创造力。此外，该框架支持用户参与的多模态互动创作，为文化创作中的人机协同开辟了新路径。
原文摘要 · Abstract (English)
Creative story generation has long been a goal of NLP research. While existing methodologies have aimed to generate long and coherent stories, they fall significantly short of human capabilities in terms of diversity and character depth. To address this, we introduce a novel story generation framework called CCI (Character-centric Creative story generation via Imagination). CCI features two modules for creative story generation: IG (Image-Guided Imagination) and MW (Multi-Writer model). In the IG module, we utilize a text-to-image model to create visual representations of key story elements, such as characters, backgrounds, and main plots, in a more novel and concrete manner than text-only approaches. The MW module uses these story elements to generate multiple persona-description candidates and selects the best one to insert into the story, thereby enhancing the richness and depth of the narrative. We compared the stories generated by CCI and baseline models through statistical analysis, as well as human and LLM evaluations. The results showed that the IG and MW modules significantly improve various aspects of the stories' creativity. Furthermore, our framework enables interactive multi-modal story generation with users, opening up new possibilities for human-LLM integration in cultural development. Project page : https://www.2024cci.p-e.kr/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。