Paper2Poster

What does this agent do, and when should you use it?

Paper2Poster is a multimodal poster-automation project for scientific papers, centered on a system called PosterAgent. It takes a paper.pdf input and produces an editable poster.pptx through a top-down, visual-in-the-loop multi-agent workflow. The README describes Parser, Planner, and a Painter-Commentor loop: paper assets are organized, text and visuals are laid out, then rendered panels are refined with VLM feedback. The project documents API-based GPT-4o configurations as well as vLLM-hosted open-model combinations, alongside Docker deployment, a Gradio demo, YAML styling, and logo support. It also exposes evaluation commands for PaperQuiz, VLM-as-Judge, and statistical measures.

Place a paper at {dataset_dir}/{paper_name}/paper.pdf and run python -m PosterAgent.new_pipeline. The command accepts --poster_path, --model_name_t for the text model, --model_name_v for the vision model, and poster dimensions, then generates poster.pptx; documented examples use 48 by 36 inches. Section-content generation can run in parallel with --max_workers. With --conference_venue, it first checks logo_store/institutes/ and logo_store/conferences/, then can search DuckDuckGo if no local logo is found, or use Google Custom Search with --use_google_search and configured credentials; explicit --institution_logo_path and --conference_logo_path options bypass detection. Styling can come from config/poster.yaml or a poster.yaml beside paper.pdf. Evaluation is run through python -m Paper2Poster-eval.eval_poster_pipeline with qa, judge, or stats metrics.

1. A researcher preparing for a conference has a paper.pdf and needs an academic poster that remains editable in PowerPoint.
2. A lab is processing several papers and wants to use --max_workers to generate section content in parallel.
3. A team with a vLLM deployment wants to use Qwen-2.5-7B-Instruct for text generation and pair it with GPT-4o or a local vision model.
4. A conference presenter needs a poster with a NeurIPS or institutional logo, using automatic lookup or supplied logo files.
5. An evaluator wants to compare generated posters on the Paper2Poster-data dataset with PaperQuiz, VLM-as-Judge, or statistical metrics.

How do you install or deploy this agent?

Install Python dependencies:

pip install -r requirements.txt

Install LibreOffice:

sudo apt install libreoffice

Install poppler:

conda install -c conda-forge poppler

Create .env in the project root and set:

OPENAI_API_KEY=<your_openai_api_key>

For Google Custom Search logo lookup, also set GOOGLE_SEARCH_API_KEY and GOOGLE_SEARCH_ENGINE_ID. Before using open-source models, deploy them with vLLM and ensure the port is configured in get_agent_config() in utils/wei_utils.py.

How do you use this agent?

After creating {dataset_dir}/{paper_name}/paper.pdf, a documented GPT-4o invocation is:

python -m PosterAgent.new_pipeline \
--poster_path="${dataset_dir}/${paper_name}/paper.pdf" \
--model_name_t="4o" \
--model_name_v="4o" \
--poster_width_inches=48 \
--poster_height_inches=36

The generated file is poster.pptx. For Docker, first run docker build -t paper2poster .copy, then use the documented docker run example to mount Paper2Poster-data and an output directory and provide OPENAI_API_KEY. For preparing poster-ready paper content only, the repository also provides a lightweight skill in skills/; its documented Codex invocation is: Use $paper2poster-poster to turn this paper into a poster package.

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

How does FollowAgents rate this agent?

[![FARS review](https://followagents.com/badge/paper2poster.svg)](https://followagents.com/en/agents/paper2poster)

<a href="https://followagents.com/en/agents/paper2poster"><img src="https://followagents.com/badge/paper2poster.svg" alt="FARS review"></a>

FAQ