← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1578 · 记录 #1
Evaluating Visual Narrative Coherence in Story Visualization via Diversified Storylines
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:52
摘要
Story visualization requires generating a coherent sequence of images that collectively form a narrative, yet existing evaluation metrics and datasets often overlook visual continuity and narrative diversity. In this paper, we introduce the Visual Context-Aware Metric for Story Visualization, which uses large vision-language models to jointly assess caption fidelity and inter-image consistency, achieving Spearman’s correlation comparable to human agreement on two benchmarks. Also, to address the shortcomings of narrowly defined datasets with low diversity, we propose a diffusion-augmented evaluation pipeline that blends diverse and controlled narrative elements at adjustable ratios, producing challenging evaluation sets. By combining VCMS with this pipeline, we provide a scalable, human-aligned framework for evaluating story visualization models.
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记录 #1 · 2026-07 · 最新观测版本
Evaluating Visual Narrative Coherence in Story Visualization via Diversified Storylines
Story visualization requires generating a coherent sequence of images that collectively form a narrative, yet existing evaluation metrics and datasets often overlook visual continuity and narrative diversity. In this paper, we introduce the Visual Context-Aware Metric for Story Visualization, which uses large vision-language models to jointly assess caption fidelity and inter-image consistency, achieving Spearman’s correlation comparable to human agreement on two benchmarks. Also, to address the shortcomings of narrowly defined datasets with low diversity, we propose a diffusion-augmented evaluation pipeline that blends diverse and controlled narrative elements at adjustable ratios, producing challenging evaluation sets. By combining VCMS with this pipeline, we provide a scalable, human-aligned framework for evaluating story visualization models.
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