# A Character-Centric Creative Story Generation via Imagination

arXiv: https://arxiv.org/abs/2409.16667 · HTML: https://mlsanity.com/paper/2409.16667/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 73 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 30 of 38)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Making LLaMA SEE and Draw with SEED Tokenizer](https://mlsanity.com/paper/2310.01218/) (2023)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [Video Generation From Text](https://mlsanity.com/paper/1710.00421/) (2017)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://mlsanity.com/paper/1812.02784/) (2018)
- [Grounding Language Models to Images for Multimodal Inputs and Outputs](https://mlsanity.com/paper/2301.13823/) (2023)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Make-A-Story: Visual Memory Conditioned Consistent Story Generation](https://mlsanity.com/paper/2211.13319/) (2022)
- [StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation](https://mlsanity.com/paper/2209.06192/) (2022)
- [TaleCrafter: Interactive Story Visualization with Multiple Characters](https://mlsanity.com/paper/2305.18247/) (2023)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Engaging Image Captioning Via Personality](https://mlsanity.com/paper/1810.10665/) (2018)
- [Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation](https://mlsanity.com/paper/1904.02357/) (2019)
- [Modeling Protagonist Emotions for Emotion-Aware Storytelling](https://mlsanity.com/paper/2010.06822/) (2020)
- [PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation](https://mlsanity.com/paper/2203.09100/) (2022)
- [StoryGPT-V: Large Language Models as Consistent Story Visualizers](https://mlsanity.com/paper/2312.02252/) (2023)
- [Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation](https://mlsanity.com/paper/1805.08191/) (2018)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation](https://mlsanity.com/paper/2404.18919/) (2024)
- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation](https://mlsanity.com/paper/2204.10703/) (2022)
- [ControlLM: Crafting Diverse Personalities for Language Models](https://mlsanity.com/paper/2402.10151/) (2024)

## Built on by (most cited first, 12 of 12)

- [Boosting Relational Deep Learning with Pretrained Tabular Models](https://mlsanity.com/paper/2504.04934/) (2025)
- [Avoidance Decoding for Diverse Multi-Branch Story Generation](https://mlsanity.com/paper/2509.02170/) (2025)
- [Using AI for User Representation: An Analysis of 83 Persona Prompts](https://mlsanity.com/paper/2508.13047/) (2025)
- [StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation](https://mlsanity.com/paper/2505.10292/) (2025)
- [Scaffolding Recursive Divergence and Convergence in Story Ideation](https://mlsanity.com/paper/2507.03307/) (2025)
- [A Universal Avoidance Method for Diverse Multi-branch Generation](https://mlsanity.com/paper/2604.17323/) (2026)
- [StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment](https://mlsanity.com/paper/2605.28073/) (2026)
- [Narrative Keyframing for Generative Creative Writing](https://mlsanity.com/paper/2608.10337/) (2026)
- [VOPE: Revisiting Hallucination of Vision-Language Models in Voluntary Imagination Task](https://mlsanity.com/paper/2511.13420/) (2025)
- [BiT-MCTS: A Theme-based Bidirectional MCTS Approach to Chinese Fiction Generation](https://mlsanity.com/paper/2603.14410/) (2026)
- [Controllable Narrative Rendering for Enhanced Assisted Writing](https://mlsanity.com/paper/2607.00009/) (2026)
- [Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity](https://mlsanity.com/paper/2608.13430/) (2026)

## Abstract (arXiv)

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/