AutoPresent: Designing Structured Visuals from Scratch
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.
677d530bbc194dfe381d3e0b
Jiaxin Ge
677d530bbc194dfe381d3e0c
Zora Zhiruo Wang
677d530bbc194dfe381d3e0d
Xuhui Zhou
677d530bbc194dfe381d3e0e
Yi-Hao Peng
claimed_verified
2025-01-09T20:01:43.124Z
63edc468679c2cc40abd4b16
https://cdn-avatars.huggingface.co/v1/production/uploads/63edc468679c2cc40abd4b16/G6dSJ_tFG28QIRt9MaxHv.jpeg
Yi-Hao
user
yihaopeng
677d530bbc194dfe381d3e0f
Sanjay Subramanian
677d530bbc194dfe381d3e10
Qinyue Tan
677d530bbc194dfe381d3e11
Maarten Sap
677d530bbc194dfe381d3e12
Alane Suhr
677d530bbc194dfe381d3e13
Daniel Fried
677d530bbc194dfe381d3e14
Graham Neubig
677d530bbc194dfe381d3e15
Trevor Darrell
AutoPresent: Designing Structured Visuals from Scratch
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.
AutoPresent, an 8B Llama-based model, generates interactive presentation slides from natural language instructions, outperforming traditional end-to-end image generation methods and achieving quality comparable to GPT-4o through iterative refinement.
SlidesBench end-to-end image generation program generation AutoPresent Llama-based model iterative design refinement natural language instructions slide generation