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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.00912 |
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| _version_ | 1866918065065689088 |
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| author | Ge, Jiaxin Wang, Zora Zhiruo Zhou, Xuhui Peng, Yi-Hao Subramanian, Sanjay Tan, Qinyue Sap, Maarten Suhr, Alane Fried, Daniel Neubig, Graham Darrell, Trevor |
| author_facet | Ge, Jiaxin Wang, Zora Zhiruo Zhou, Xuhui Peng, Yi-Hao Subramanian, Sanjay Tan, Qinyue Sap, Maarten Suhr, Alane Fried, Daniel Neubig, Graham Darrell, Trevor |
| contents | Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i)reference-based to measure similarity to a target slide, and (ii)reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoPresent: Designing Structured Visuals from Scratch Ge, Jiaxin Wang, Zora Zhiruo Zhou, Xuhui Peng, Yi-Hao Subramanian, Sanjay Tan, Qinyue Sap, Maarten Suhr, Alane Fried, Daniel Neubig, Graham Darrell, Trevor Computer Vision and Pattern Recognition Computation and Language Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i)reference-based to measure similarity to a target slide, and (ii)reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals. |
| title | AutoPresent: Designing Structured Visuals from Scratch |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2501.00912 |