Research map / Large language models / Agents, code and math
NarrativePlay: An Automated System for Crafting Visual Worlds in Novels for Role-Playing
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Presents NarrativePlay, a web demo that extracts a novel's main storyline events and characters with an LLM and turns them into an interactive role-play experience with generated scene images, portraits and speech.
Problem
Role-playing agents in narratives usually rely on handcrafted sandboxes and manual character/environment settings, which are labour-intensive and not generalisable to new stories.
Approach
Automatically extract character traits and main storyline events from a narrative with an LLM, let the user pick a character, and generate event-specific environment visuals, character portraits and speech so the user experiences the story from that character's perspective.
Key results
- No quantitative evaluation is reported; the paper is a system demonstration of the web platform.
Limitations
- No quantitative or user evaluation of the system is reported.
- Relies on closed commercial services (gpt-3.5-turbo, Midjourney, Hotpot AI, FakeYou) that limit reproducibility.
Experimental setup
Datasets: user-provided narratives (novels)
Builds on: Generative Agents
Closest papers by meaning
- CoSER: Coordinating LLM-Based Persona Simulation of Established Roles (ICML 2025 · similarity 0.85)
- Unbounded: A Generative Infinite Game of Character Life Simulation (ICLR 2025 · similarity 0.85)
- StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models (AAAI 2026 · similarity 0.85)
- Codifying Character Logic in Role-Playing (NeurIPS 2025 · similarity 0.84)
- TALES: Text-Adventure Learning Environment Suite (NeurIPS 2026 · similarity 0.84)
- MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents (ICLR 2025 · similarity 0.84)
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