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Convert academic papers into interactive websites, presentation videos, and conference posters - autonomous pipeline for LaTeX/PDF transformation.

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Paper2All - Academic Paper Transformation

Convert academic papers (LaTeX or PDF) into multiple promotional and presentation formats: interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster).

Key Benefits

  • Transform papers into three output formats automatically
  • LLM-powered content extraction and layout design
  • Interactive, explorable academic homepages
  • Professional presentation videos with narration
  • Print-ready conference posters (any size)
  • Batch processing for multiple papers
  • Institution branding and logo discovery

Core Capabilities

  • Paper2Web: Responsive multi-section websites with interactive figures, tables, citations
  • Paper2Video: Presentation videos with slides, speech synthesis, cursor movements, optional talking-head
  • Paper2Poster: Custom dimension posters with professional layouts and QR codes
  • Input Support: LaTeX source (recommended) or high-quality PDF
  • Multi-language: English/Chinese content generation
  • Quality Assessment: Automated checks for completeness, accuracy, aesthetics

When to Use

  • Creating conference materials (posters, videos, websites)
  • Promoting research findings online
  • Enhancing preprints on arXiv/bioRxiv
  • Generating video abstracts for journals
  • Disseminating findings on social media
  • Batch processing multiple papers

Workflow

  1. Install Paper2All and configure API keys (OpenAI)
  2. Organize paper directory with LaTeX/PDF and figures
  3. Run pipeline: python pipeline_all.py --input-dir paper/ --output-dir output/
  4. Choose components: website, poster, video, or all three
  5. Review and deploy generated outputs

Technical Requirements

  • Python 3.11, LibreOffice, Poppler
  • OpenAI API key (GPT-4 recommended)
  • Optional: NVIDIA GPU (48GB) for talking-head videos
  • Processing: 15-60 minutes per paper depending on components

Source: https://github.com/YuhangChen1/Paper2All License: MIT

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