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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.13452 |
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| _version_ | 1866915937971601408 |
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| author | Mondal, Ishani Song, Yiwen Parmar, Mihir Goyal, Palash Boyd-Graber, Jordan Pfister, Tomas Song, Yale |
| author_facet | Mondal, Ishani Song, Yiwen Parmar, Mihir Goyal, Palash Boyd-Graber, Jordan Pfister, Tomas Song, Yale |
| contents | Long-form visual storytelling requires maintaining continuity across shots, including consistent characters, stable environments, and smooth scene transitions. While existing generative models can produce strong individual frames, they fail to preserve such continuity, leading to appearance changes, inconsistent backgrounds, and abrupt scene shifts. We introduce CANVAS (Continuity-Aware Narratives via Visual Agentic Storyboarding), a multi-agent framework that explicitly plans visual continuity in multi-shot narratives. CANVAS enforces coherence through character continuity, persistent background anchors, and location-aware scene planning for smooth transitions within the same setting We evaluate CANVAS on two storyboard generation benchmarks ST-BENCH and ViStoryBench and introduce a new challenging benchmark HardContinuityBench for long-range narrative consistency. CANVAS consistently outperforms the best-performing baseline, improving background continuity by 21.6%, character consistency by 9.6% and props consistency by 7.6%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13452 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CANVAS: Continuity-Aware Narratives via Visual Agentic Storyboarding Mondal, Ishani Song, Yiwen Parmar, Mihir Goyal, Palash Boyd-Graber, Jordan Pfister, Tomas Song, Yale Computation and Language Long-form visual storytelling requires maintaining continuity across shots, including consistent characters, stable environments, and smooth scene transitions. While existing generative models can produce strong individual frames, they fail to preserve such continuity, leading to appearance changes, inconsistent backgrounds, and abrupt scene shifts. We introduce CANVAS (Continuity-Aware Narratives via Visual Agentic Storyboarding), a multi-agent framework that explicitly plans visual continuity in multi-shot narratives. CANVAS enforces coherence through character continuity, persistent background anchors, and location-aware scene planning for smooth transitions within the same setting We evaluate CANVAS on two storyboard generation benchmarks ST-BENCH and ViStoryBench and introduce a new challenging benchmark HardContinuityBench for long-range narrative consistency. CANVAS consistently outperforms the best-performing baseline, improving background continuity by 21.6%, character consistency by 9.6% and props consistency by 7.6%. |
| title | CANVAS: Continuity-Aware Narratives via Visual Agentic Storyboarding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.13452 |