LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization

TL;DR

LogiStory introduces a logic-aware framework for multi-image story visualization that explicitly models visual logic, improving narrative coherence and visual quality in generated stories.

Contribution

The paper proposes a novel multi-agent system that explicitly models visual logic, bridging story planning with visual generation for clearer and more coherent visual stories.

Findings

Significant improvement in narrative logic of generated stories.

Enhanced visual quality and coherence in multi-image story generation.

Introduction of the LogicTale benchmark for evaluating visual logic.

Abstract

Generating coherent and communicative visual sequences, such as image sequences and videos, remains a significant challenge for current multimodal systems. Despite advances in visual quality and the integration of world knowledge, existing models still struggle to maintain logical flow, often resulting in disjointed actions, fragmented narratives, and unclear storylines. We attribute these issues to the lack of attention to visual logic, a critical yet underexplored dimension of visual sequence generation that we define as the perceptual and causal coherence among characters, actions, and scenes over time. To bridge this gap, we propose a logic-aware multi-image story visualization framework, LogiStory. The framework is built around the central innovation of explicitly modeling visual logic in story visualization. To realize this idea, we design a multi-agent system that grounds roles, extracts causal chains, and verifies story-level consistency, transforming narrative coherence from an implicit byproduct of image generation into an explicit modeling objective. This design effectively bridges structured story planning with visual generation, enhancing both narrative clarity and visual quality in story visualization. Furthermore, to evaluate the generation capacity, we construct LogicTale, a benchmark comprising richly annotated stories, emphasizing causal reasoning, and visual logic interpretability. We establish comprehensive automatic and human evaluation protocols designed to measure both visual logic and perceptual quality. Experiments demonstrate that our approach significantly improves the narrative logic of generated visual stories. This work provides a foundational step towards modeling and enforcing visual logic in general image sequence and video generation tasks.