A Character-Centric Creative Story Generation via Imagination

• URL: http://arxiv.org/abs/2409.16667v2
• Date: Tue, 15 Oct 2024 06:12:29 GMT
• Title: A Character-Centric Creative Story Generation via Imagination
• Authors: Kyeongman Park, Minbeom Kim, Kyomin Jung,
• Abstract summary: 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. 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.
• Score: 15.345466372805516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

Related papers

• Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation [0.0]
  Authors encounter moments of creative block, where the path forward in their narrative becomes obscured. This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts. By inputting a short story prompt, users can receive a conclusion to their story, articulated in one sentence or more, thereby enhancing the storytelling process with AI-driven creativity.
  arXiv  Detail & Related papers  (2024-10-04T18:56:32Z)
• Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
  We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents. We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
  arXiv  Detail & Related papers  (2024-10-03T15:44:42Z)
• Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
  We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions. Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark. We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
  arXiv  Detail & Related papers  (2024-09-20T14:56:33Z)
• SEED-Story: Multimodal Long Story Generation with Large Language Model [66.37077224696242]
  SEED-Story is a novel method that leverages a Multimodal Large Language Model (MLLM) to generate extended multimodal stories. We propose multimodal attention sink mechanism to enable the generation of stories with up to 25 sequences (only 10 for training) in a highly efficient autoregressive manner. We present a large-scale and high-resolution dataset named StoryStream for training our model and quantitatively evaluating the task of multimodal story generation in various aspects.
  arXiv  Detail & Related papers  (2024-07-11T17:21:03Z)
• MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation [50.01780173691132]
  We introduce Modular Story Premise Synthesis (MoPS) MoPS breaks down story premises into modules like background and persona for automated design and generation. Thorough evaluations demonstrate that our synthesized premises excel in diversity, fascination, completeness, and originality.
  arXiv  Detail & Related papers  (2024-06-09T08:31:14Z)
• SARD: A Human-AI Collaborative Story Generation [0.0]
  We propose SARD, a drag-and-drop visual interface for generating a multi-chapter story using large language models. Our evaluation of the usability of SARD and its creativity support shows that while node-based visualization of the narrative may help writers build a mental model, it exerts unnecessary mental overhead to the writer. We also found that AI generates stories that are less lexically diverse, irrespective of the complexity of the story.
  arXiv  Detail & Related papers  (2024-03-03T17:48:42Z)
• GENEVA: GENErating and Visualizing branching narratives using LLMs [15.43734266732214]
  textbfGENEVA, a prototype tool, generates a rich narrative graph with branching and reconverging storylines. textbfGENEVA has the potential to assist in game development, simulations, and other applications with game-like properties.
  arXiv  Detail & Related papers  (2023-11-15T18:55:45Z)
• Telling Creative Stories Using Generative Visual Aids [52.623545341588304]
  We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as story writing aid wrote significantly more creative, original, complete and visualizable stories. Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.
  arXiv  Detail & Related papers  (2021-10-27T23:13:47Z)
• FairyTailor: A Multimodal Generative Framework for Storytelling [33.39639788612019]
  We introduce a system and a demo, FairyTailor, for human-in-the-loop visual story co-creation. Users can create a cohesive children's fairytale by weaving generated texts and retrieved images with their input. To our knowledge, this is the first dynamic tool for multimodal story generation that allows interactive co-formation of both texts and images.
  arXiv  Detail & Related papers  (2021-07-13T02:45:08Z)
• Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning [12.264880519328353]
  We introduce Commonsense-inference Augmented neural StoryTelling (CAST), a framework for introducing commonsense reasoning into the generation process. We find that our CAST method produces significantly more coherent, on-topic, enjoyable and fluent stories than existing models in both the single-character and two-character settings.
  arXiv  Detail & Related papers  (2021-05-04T06:40:33Z)
• Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling [81.33107307509718]
  We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
  arXiv  Detail & Related papers  (2020-08-11T03:55:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.