Generating Animated Layouts as Structured Text Representations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915270139838464 |
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| author | Shin, Yeonsang Kim, Jihwan Song, Yumin Lee, Kyungseung Chung, Hyunhee Na, Taeyoung |
| author_facet | Shin, Yeonsang Kim, Jihwan Song, Yumin Lee, Kyungseung Chung, Hyunhee Na, Taeyoung |
| contents | Despite the remarkable progress in text-to-video models, achieving precise control over text elements and animated graphics remains a significant challenge, especially in applications such as video advertisements. To address this limitation, we introduce Animated Layout Generation, a novel approach to extend static graphic layouts with temporal dynamics. We propose a Structured Text Representation for fine-grained video control through hierarchical visual elements. To demonstrate the effectiveness of our approach, we present VAKER (Video Ad maKER), a text-to-video advertisement generation pipeline that combines a three-stage generation process with Unstructured Text Reasoning for seamless integration with LLMs. VAKER fully automates video advertisement generation by incorporating dynamic layout trajectories for objects and graphics across specific video frames. Through extensive evaluations, we demonstrate that VAKER significantly outperforms existing methods in generating video advertisements. Project Page: https://yeonsangshin.github.io/projects/Vaker |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00975 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Animated Layouts as Structured Text Representations Shin, Yeonsang Kim, Jihwan Song, Yumin Lee, Kyungseung Chung, Hyunhee Na, Taeyoung Computer Vision and Pattern Recognition Despite the remarkable progress in text-to-video models, achieving precise control over text elements and animated graphics remains a significant challenge, especially in applications such as video advertisements. To address this limitation, we introduce Animated Layout Generation, a novel approach to extend static graphic layouts with temporal dynamics. We propose a Structured Text Representation for fine-grained video control through hierarchical visual elements. To demonstrate the effectiveness of our approach, we present VAKER (Video Ad maKER), a text-to-video advertisement generation pipeline that combines a three-stage generation process with Unstructured Text Reasoning for seamless integration with LLMs. VAKER fully automates video advertisement generation by incorporating dynamic layout trajectories for objects and graphics across specific video frames. Through extensive evaluations, we demonstrate that VAKER significantly outperforms existing methods in generating video advertisements. Project Page: https://yeonsangshin.github.io/projects/Vaker |
| title | Generating Animated Layouts as Structured Text Representations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.00975 |