Paper2All: Academic Paper Transformation Pipeline

Overview

This skill enables the transformation of academic papers into multiple promotional and presentation formats using the Paper2All autonomous pipeline. The system converts research papers (LaTeX or PDF) into three primary outputs:

1. Paper2Web: Interactive, explorable academic homepages with layout-aware design
2. Paper2Video: Professional presentation videos with narration, slides, and optional talking-head
3. Paper2Poster: Print-ready conference posters with professional layouts

The pipeline uses LLM-powered content extraction, design generation, and iterative refinement to create high-quality outputs suitable for conferences, journals, preprint repositories, and academic promotion.

When to Use This Skill

Use this skill when:

• Creating conference materials: Posters, presentation videos, and companion websites for academic conferences
• Promoting research: Converting published papers or preprints into accessible, engaging web formats
• Preparing presentations: Generating video abstracts or full presentation videos from paper content
• Disseminating findings: Creating promotional materials for social media, lab websites, or institutional showcases
• Enhancing preprints: Adding interactive homepages to bioRxiv, arXiv, or other preprint submissions
• Batch processing: Generating promotional materials for multiple papers simultaneously

Trigger phrases:

• "Convert this paper to a website"
• "Generate a conference poster from my LaTeX paper"
• "Create a video presentation from this research"
• "Make an interactive homepage for my paper"
• "Transform my paper into promotional materials"
• "Generate a poster and video for my conference talk"

Core Capabilities

1. Paper2Web: Interactive Website Generation

Converts papers into layout-aware, interactive academic homepages that go beyond simple HTML conversion.

Key Features:

• Responsive, multi-section layouts adapted to paper content
• Interactive figures, tables, and citations
• Mobile-friendly design with navigation
• Automatic logo discovery (with Google Search API)
• Aesthetic refinement and quality assessment

Best For: Post-publication promotion, preprint enhancement, lab websites, permanent research showcases

→ See references/paper2web.md for detailed documentation

2. Paper2Video: Presentation Video Generation

Generates professional presentation videos with slides, narration, cursor movements, and optional talking-head video.

Key Features:

• Automated slide generation from paper structure
• Natural-sounding speech synthesis
• Synchronized cursor movements and highlights
• Optional talking-head video using Hallo2 (requires GPU)
• Multi-language support

Best For: Video abstracts, conference presentations, online talks, course materials, YouTube promotion

→ See references/paper2video.md for detailed documentation

3. Paper2Poster: Conference Poster Generation

Creates print-ready academic posters with professional layouts and visual design.

Key Features:

• Custom poster dimensions (any size)
• Professional design templates
• Institution branding support
• QR code generation for links
• High-resolution output (300+ DPI)

Best For: Conference poster sessions, symposiums, academic exhibitions, virtual conferences

→ See references/paper2poster.md for detailed documentation

Quick Start

Prerequisites

1. Install Paper2All:

   git clone https://github.com/YuhangChen1/Paper2All.git
   cd Paper2All
   uv pip install -r requirements.txt
   

2. Configure API Keys (create .env file):

   OPENAI_API_KEY=your_openai_api_key_here
   # Optional: GOOGLE_API_KEY and GOOGLE_CSE_ID for logo search
   

3. Install System Dependencies:

   • LibreOffice (document conversion)
   • Poppler utilities (PDF processing)
   • NVIDIA GPU with 48GB (optional, for talking-head videos)

→ See references/installation.md for complete installation guide

Basic Usage

Generate All Components (website + poster + video):

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1

Generate Website Only:

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-website

Generate Poster with Custom Size:

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-poster \
  --poster-width-inches 60 \
  --poster-height-inches 40

Generate Video (lightweight pipeline):

python pipeline_light.py \
  --model_name_t gpt-4.1 \
  --model_name_v gpt-4.1 \
  --result_dir "path/to/output" \
  --paper_latex_root "path/to/paper"

→ See references/usage_examples.md for comprehensive workflow examples

Workflow Decision Tree

Use this decision tree to determine which components to generate:

User needs promotional materials for paper?
│
├─ Need permanent online presence?
│  └─→ Generate Paper2Web (interactive website)
│
├─ Need physical conference materials?
│  ├─→ Poster session? → Generate Paper2Poster
│  └─→ Oral presentation? → Generate Paper2Video
│
├─ Need video content?
│  ├─→ Journal video abstract? → Generate Paper2Video (5-10 min)
│  ├─→ Conference talk? → Generate Paper2Video (15-20 min)
│  └─→ Social media? → Generate Paper2Video (1-3 min)
│
└─ Need complete package?
   └─→ Generate all three components

Input Requirements

Supported Input Formats

1. LaTeX Source (Recommended):

paper_directory/
├── main.tex              # Main paper file
├── sections/             # Optional: split sections
├── figures/              # All figure files
├── tables/               # Table files
└── bibliography.bib      # References

2. PDF:

• High-quality PDF with embedded fonts
• Selectable text (not scanned images)
• High-resolution figures (300+ DPI preferred)

Input Organization

Single Paper:

input/
└── paper_name/
    ├── main.tex (or paper.pdf)
    ├── figures/
    └── bibliography.bib

Multiple Papers (batch processing):

input/
├── paper1/
│   └── main.tex
├── paper2/
│   └── main.tex
└── paper3/
    └── main.tex

Common Parameters

Model Selection

• --model-choice 1: GPT-4 (best balance of quality and cost)
• --model-choice 2: GPT-4.1 (latest features, higher cost)
• --model_name_t gpt-3.5-turbo: Faster, lower cost (acceptable quality)

Component Selection

• --generate-website: Enable website generation
• --generate-poster: Enable poster generation
• --generate-video: Enable video generation
• --enable-talking-head: Add talking-head to video (requires GPU)

Customization

• --poster-width-inches [width]: Custom poster width
• --poster-height-inches [height]: Custom poster height
• --video-duration [seconds]: Target video length
• --enable-logo-search: Automatic institution logo discovery

Output Structure

Generated outputs are organized by paper and component:

output/
└── paper_name/
    ├── website/
    │   ├── index.html
    │   ├── styles.css
    │   └── assets/
    ├── poster/
    │   ├── poster_final.pdf
    │   ├── poster_final.png
    │   └── poster_source/
    └── video/
        ├── final_video.mp4
        ├── slides/
        ├── audio/
        └── subtitles/

Best Practices

Input Preparation

1. Use LaTeX when possible: Provides best content extraction and structure
2. Organize files properly: Keep all assets (figures, tables, bibliography) in paper directory
3. High-quality figures: Use vector formats (PDF, SVG) or high-resolution rasters (300+ DPI)
4. Clean LaTeX: Remove compilation artifacts, ensure source compiles successfully

Model Selection Strategy

• GPT-4: Best for production-quality outpu