title: AutoPresent: Designing Structured Visuals from Scratch
summary: 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.
paper_authors._id: 677d530bbc194dfe381d3e0b
paper_authors.name: Jiaxin Ge
paper_authors._id: 677d530bbc194dfe381d3e0c
paper_authors.name: Zora Zhiruo Wang
paper_authors._id: 677d530bbc194dfe381d3e0d
paper_authors.name: Xuhui Zhou
paper_authors._id: 677d530bbc194dfe381d3e0e
paper_authors.name: Yi-Hao Peng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T20:01:43.124Z
paper_authors._id: 63edc468679c2cc40abd4b16
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63edc468679c2cc40abd4b16/G6dSJ_tFG28QIRt9MaxHv.jpeg
paper_authors.fullname: Yi-Hao
paper_authors.type: user
paper_authors.user: yihaopeng
paper_authors._id: 677d530bbc194dfe381d3e0f
paper_authors.name: Sanjay Subramanian
paper_authors._id: 677d530bbc194dfe381d3e10
paper_authors.name: Qinyue Tan
paper_authors._id: 677d530bbc194dfe381d3e11
paper_authors.name: Maarten Sap
paper_authors._id: 677d530bbc194dfe381d3e12
paper_authors.name: Alane Suhr
paper_authors._id: 677d530bbc194dfe381d3e13
paper_authors.name: Daniel Fried
paper_authors._id: 677d530bbc194dfe381d3e14
paper_authors.name: Graham Neubig
paper_authors._id: 677d530bbc194dfe381d3e15
paper_authors.name: Trevor Darrell
paper_title: AutoPresent: Designing Structured Visuals from Scratch
paper_summary: 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.
paper_ai_keywords: SlidesBench benchmark slide generation natural language (NL) reference-based evaluation reference-free evaluation end-to-end image generation program generation Llama-based model AutoPresent user-interactable formats iterative design refinement