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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2503.02399 |
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| _version_ | 1866929740493881344 |
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| author | Kim, Seungkwon Park, GyuTae Kim, Sangyeon Nam, Seung-Hun |
| author_facet | Kim, Seungkwon Park, GyuTae Kim, Sangyeon Nam, Seung-Hun |
| contents | Story visualization is the transformation of narrative elements into image sequences. While existing research has primarily focused on visual contextual coherence, the deeper narrative essence of stories often remains overlooked. This limitation hinders the practical application of these approaches, as generated images frequently fail to capture the intended meaning and nuances of the narrative fully. To address these challenges, we propose VisAgent, a training-free multi-agent framework designed to comprehend and visualize pivotal scenes within a given story. By considering story distillation, semantic consistency, and contextual coherence, VisAgent employs an agentic workflow. In this workflow, multiple specialized agents collaborate to: (i) refine layered prompts based on the narrative structure and (ii) seamlessly integrate \gt{generated} elements, including refined prompts, scene elements, and subject placement, into the final image. The empirically validated effectiveness confirms the framework's suitability for practical story visualization applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VisAgent: Narrative-Preserving Story Visualization Framework Kim, Seungkwon Park, GyuTae Kim, Sangyeon Nam, Seung-Hun Computer Vision and Pattern Recognition Artificial Intelligence Story visualization is the transformation of narrative elements into image sequences. While existing research has primarily focused on visual contextual coherence, the deeper narrative essence of stories often remains overlooked. This limitation hinders the practical application of these approaches, as generated images frequently fail to capture the intended meaning and nuances of the narrative fully. To address these challenges, we propose VisAgent, a training-free multi-agent framework designed to comprehend and visualize pivotal scenes within a given story. By considering story distillation, semantic consistency, and contextual coherence, VisAgent employs an agentic workflow. In this workflow, multiple specialized agents collaborate to: (i) refine layered prompts based on the narrative structure and (ii) seamlessly integrate \gt{generated} elements, including refined prompts, scene elements, and subject placement, into the final image. The empirically validated effectiveness confirms the framework's suitability for practical story visualization applications. |
| title | VisAgent: Narrative-Preserving Story Visualization Framework |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.02399 |