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A Character-Centric Creative Story Generation via Imagination

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arxiv 2409.16667 v3 pith:GDJ5LQUE submitted 2024-09-25 cs.CL

classification cs.CL
keywords storygenerationcreativeimaginationstoriescharacter-centricdepthelements
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VOPE: Revisiting Hallucination of Vision-Language Models in Voluntary Imagination Task

    cs.CV 2025-11 conditional novelty 7.0 of 10

    A recheck-based evaluation shows LVLMs often fail to correctly judge the presence of objects they themselves generated, and current mitigation methods do not fix this.

  2. Scaffolding Recursive Divergence and Convergence in Story Ideation

    cs.HC 2025-07 reject novelty 6.0 of 10

    Reverger scaffolds recursive divergence and multi-direction convergence in story ideation, with a user study whose headline significance statistics are internally impossible.

  3. Avoidance Decoding for Diverse Multi-Branch Story Generation

    cs.CL 2025-09 conditional novelty 5.0 of 10

    Avoidance Decoding penalizes token choices that resemble previously generated story branches, using a hybrid concept-level and narrative-level similarity penalty, and reports large diversity gains across several LLMs.

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