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Generating Animated Layouts as Structured Text Representations

arXiv 2505.00975 ↗First posted 2 May 2025cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 34 references that are in the map. First posted on arXiv 2 May 2025.

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27 of 34 references.

References · 27

  1. 2023GPT-4 Technical Reportcited by 18,396
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  5. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
  6. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,261
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  8. 2021Associating Objects with Transformers for Video Object Segmentationcited by 226
  9. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 143
  10. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 123
  11. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 95
  12. 2021Variational Transformer Networks for Layout Generationcited by 88
15 more
  1. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  2. 2017Automatic Understanding of Image and Video Advertisementscited by 68
  3. 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 68
  4. 2023PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layoutcited by 61
  5. 2022Composition-aware Graphic Layout GAN for Visual-textual Presentation Designscited by 59
  6. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
  7. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 48
  8. 2023Unifying Layout Generation with a Decoupled Diffusion Modelcited by 41
  9. 2024A Vision Check-up for Language Modelscited by 36
  10. 2023Towards Flexible Multi-modal Document Modelscited by 31
  11. 2024Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraintscited by 26
  12. 2024Desigen: A Pipeline for Controllable Design Template Generationcited by 19
  13. 2023A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptionscited by 15
  14. 2023MM-AU:Towards Multimodal Understanding of Advertisement Videoscited by 8
  15. 2018Story Understanding in Video Advertisementscited by 5

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