ViStoryBench: Comprehensive Benchmark Suite for Story Visualization
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915895815700480 |
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| author | Zhuang, Cailin Huang, Ailin Hu, Yaoqi Wu, Jingwei Cheng, Wei Liao, Jiaqi Wang, Hongyuan Liao, Xinyao Cai, Weiwei Xu, Hengyuan Zhang, Xuanyang Zeng, Xianfang Huang, Zhewei Yu, Gang Zhang, Chi |
| author_facet | Zhuang, Cailin Huang, Ailin Hu, Yaoqi Wu, Jingwei Cheng, Wei Liao, Jiaqi Wang, Hongyuan Liao, Xinyao Cai, Weiwei Xu, Hengyuan Zhang, Xuanyang Zeng, Xianfang Huang, Zhewei Yu, Gang Zhang, Chi |
| contents | Story visualization aims to generate coherent image sequences that faithfully represent a narrative and match given character references. Despite progress in generative models, existing benchmarks remain narrow in scope, often limited to short prompts, lacking character references, or single-image cases, failing to reflect real-world narrative complexity and obscuring true model performance.We introduce ViStoryBench, a comprehensive benchmark designed to evaluate story visualization models across varied narrative structures, visual styles, and character settings. It features richly annotated multi-shot scripts derived from curated stories spanning literature, film, and folklore. Large language models assist in story summarization and script generation, with all outputs verified by humans for coherence and fidelity. Character references are carefully curated to maintain consistency across different artistic styles. ViStoryBench proposes a suite of multi-dimensional automated metrics to evaluate character consistency, style similarity, prompt alignment, aesthetic quality, and artifacts like copy-paste behavior. These metrics are validated through human studies and used to assess a broad range of open-source and commercial models, enabling systematic analysis and encouraging advances in visual storytelling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ViStoryBench: Comprehensive Benchmark Suite for Story Visualization Zhuang, Cailin Huang, Ailin Hu, Yaoqi Wu, Jingwei Cheng, Wei Liao, Jiaqi Wang, Hongyuan Liao, Xinyao Cai, Weiwei Xu, Hengyuan Zhang, Xuanyang Zeng, Xianfang Huang, Zhewei Yu, Gang Zhang, Chi Computer Vision and Pattern Recognition Story visualization aims to generate coherent image sequences that faithfully represent a narrative and match given character references. Despite progress in generative models, existing benchmarks remain narrow in scope, often limited to short prompts, lacking character references, or single-image cases, failing to reflect real-world narrative complexity and obscuring true model performance.We introduce ViStoryBench, a comprehensive benchmark designed to evaluate story visualization models across varied narrative structures, visual styles, and character settings. It features richly annotated multi-shot scripts derived from curated stories spanning literature, film, and folklore. Large language models assist in story summarization and script generation, with all outputs verified by humans for coherence and fidelity. Character references are carefully curated to maintain consistency across different artistic styles. ViStoryBench proposes a suite of multi-dimensional automated metrics to evaluate character consistency, style similarity, prompt alignment, aesthetic quality, and artifacts like copy-paste behavior. These metrics are validated through human studies and used to assess a broad range of open-source and commercial models, enabling systematic analysis and encouraging advances in visual storytelling. |
| title | ViStoryBench: Comprehensive Benchmark Suite for Story Visualization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.24862 |