metadata

title: P2P Paper-to-Poster Generator
emoji: 🎓
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
license: mit

P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark

🚀 Try it on Hugging Face Spaces

This application is deployed on Hugging Face Spaces! You can try it directly in your browser without any installation:

🎓 Launch P2P Paper-to-Poster Generator

Quick Start on Spaces:

Upload your PDF research paper
Enter your OpenAI API key and base URL (if using proxy)
Input the AI model name (e.g., gpt-4o-mini, claude-3-sonnet)
Configure the figure detection service URL
Click "Generate Poster" and wait for processing
Preview the generated poster and download JSON/HTML files

⚠️ Requirements:

Valid OpenAI API key with sufficient balance
Figure detection service URL for extracting images from PDFs
Compatible AI model (OpenAI, Claude, Gemini, etc.)

💡 Features:

Real-time HTML poster preview
Direct JSON structure display
Support for multiple AI models
Flexible API configuration

Overview

P2P is an AI-powered tool that automatically converts academic research papers into professional conference posters. This repository contains the code for generating and evaluating these posters, leveraging large language models to extract key information and create visually appealing presentations.

The full research paper is available on arXiv.

Note: Due to the large size of the evaluation and training datasets, only simple samples are included in this repository. The complete datasets are available on HuggingFace:

P2PInstruct - Training dataset
P2PEval - Benchmark dataset

Repository Structure

Core Files

main.py: Main entry point for generating a poster from a single paper
start.py: Batch processing script for generating posters from multiple papers
end.py: Evaluation coordinator that processes generated posters
evalv2.py: Core evaluation logic with metrics and comparison methods
figure_detection.py: Utility for detecting and extracting figures from PDFs

Directories

poster/: Core poster generation logic
- poster.py: Main poster generation implementation
- figures.py: Figure extraction and processing utilities
- compress.py: Image compression utilities
- loader.py: PDF loading utilities
eval/: Evaluation tools and resources
- eval_checklist.py: Checklist-based evaluation implementation
- predict_with_xgboost.py: ML-based poster quality prediction
- common.yaml: Common evaluation parameters
- xgboost_model.joblib: Pre-trained evaluation model

Requirements

Python 3.10+
Dependencies listed in requirements.txt

Setup

Install dependencies:

pip install -r requirements.txt
playwright install

Usage

Generating a Single Poster

To generate a poster from a single paper:

# Deploy figure_detection first
python main.py --url="URL_TO_PDF" --pdf="path/to/paper.pdf" --model="gpt-4o-mini" --output="output/poster.json"

Parameters:

--url: URL for PDF processing service (detecting and extracting figures)
--pdf: Path to the local PDF file
--model: LLM model to use (default: gpt-4o-mini)
--output: Output file path (default: poster.json)

Output Files:

poster.json: JSON representation of the poster
poster.html: HTML version of the poster
poster.png: PNG image of the poster

Batch Generating Posters

To generate posters for multiple papers:

Organize your papers in a directory structure:

eval/data/
  └─ paper_id_1/
     └─ paper.pdf
  └─ paper_id_2/
     └─ paper.pdf
  ...

Edit start.py to configure:
- url: URL for PDF processing service
- input_dir: Directory containing papers (default: "eval/data")
- models: List of AI models to use for generation
Run the batch generation script:

python start.py

Generated posters will be saved to:

eval/temp-v2/{model_name}/{paper_id}/
  └─ poster.json
  └─ poster.html
  └─ poster.png

Evaluating Posters

To evaluate generated posters:

Ensure reference materials exist:

eval/data/{paper_id}/
  └─ poster.png (reference poster)
  └─ checklist.yaml (evaluation checklist)

Run the evaluation script:

python end.py

Evaluation results will be saved to eval/temp-v2/results.jsonl.

Citation

If you find our work useful, please consider citing P2P:

@misc{sun2025p2pautomatedpapertopostergeneration,
      title={P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark}, 
      author={Tao Sun and Enhao Pan and Zhengkai Yang and Kaixin Sui and Jiajun Shi and Xianfu Cheng and Tongliang Li and Wenhao Huang and Ge Zhang and Jian Yang and Zhoujun Li},
      year={2025},
      eprint={2505.17104},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.17104}, 
}