KathaTrace: Diagnosing Semantic Trajectory Collapse in Generated Visual Narratives
KathaTrace is a generator-agnostic protocol for diagnosing semantic trajectory collapse, defined as the loss of transition meaning between scenes in visual narratives. The authors introduce KathaBench-25K, a dataset of 5,000 narratives from classical collections, and define the Semantic Trajectory Gap (STG) metric. Experiments show substantial STG (23.5±1.3) across state-of-the-art generators. Semantic Compass, an actionability probe, uses KathaTrace signals for post-generation repair and improves storyboard selection.
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[Submitted on 1 Jul 2026]
Title:KathaTrace: Diagnosing Semantic Trajectory Collapse in Generated Visual Narratives
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Abstract:Visual narratives are central to storyboards, comics, children's media, and film previsualization, where viewers understand stories from images alone. Recent generators such as StoryDiffusion produce coherent sequences, but visual coherence does not guarantee that source-story transition meaning remains recoverable. Existing benchmarks assess visual quality, content faithfulness, and scene coherence, but miss a critical failure mode: storyboards where scenes appear visually coherent while the semantic link between scenes disappears. We introduce KathaTrace, a generator-agnostic protocol for diagnosing semantic trajectory collapse, defined as the loss of transition meaning needed to understand how one scene follows another. KathaTrace evaluates transitions under three evidence conditions: text-only, image-only, and text-plus-image, and filters ambiguous items. We contribute KathaBench-25K, with 5,000 narratives from classical collections including Aesop, Panchatantra, and Kathasaritasagara, 20,000 transitions, and 28,712 recoverability questions. We define Semantic Trajectory Gap, or STG, as text-only minus image-only recoverability, measuring transition meaning lost during visualization. Human validation yields Fleiss' kappa = 0.845. Experiments across state-of-the-art generators show substantial STG of 23.5 +/- 1.3. Semantic Compass, an actionability probe, uses KathaTrace signals for post-generation repair and improves storyboard selection.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.01312 [cs.CV]
(or arXiv:2607.01312v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.01312
arXiv-issued DOI via DataCite
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From: Jamuna S Murthy [view email] [v1] Wed, 1 Jul 2026 17:51:08 UTC (15,286 KB)
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