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TaleForge: Interactive Multimodal System for Personalized Story Creation

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arxiv 2506.21832 v1 pith:LGJD7LMA submitted 2025-06-27 cs.CV

TaleForge: Interactive Multimodal System for Personalized Story Creation

classification cs.CV
keywords personalizedtaleforgegenerationsystemuserscharactercreateengagement
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Storytelling is a deeply personal and creative process, yet existing methods often treat users as passive consumers, offering generic plots with limited personalization. This undermines engagement and immersion, especially where individual style or appearance is crucial. We introduce TaleForge, a personalized story-generation system that integrates large language models (LLMs) and text-to-image diffusion to embed users' facial images within both narratives and illustrations. TaleForge features three interconnected modules: Story Generation, where LLMs create narratives and character descriptions from user prompts; Personalized Image Generation, merging users' faces and outfit choices into character illustrations; and Background Generation, creating scene backdrops that incorporate personalized characters. A user study demonstrated heightened engagement and ownership when individuals appeared as protagonists. Participants praised the system's real-time previews and intuitive controls, though they requested finer narrative editing tools. TaleForge advances multimodal storytelling by aligning personalized text and imagery to create immersive, user-centric experiences.

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Cited by 1 Pith paper

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

  1. Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents

    cs.AI 2025-10 unverdicted novelty 4.0

    Dramaturge applies a hierarchical divide-and-conquer workflow with multiple LLM agents for iterative global-to-local review and coordinated revision of narrative scripts.