paper-2-web

name	paper-2-web
description	Use when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.
allowed-tools	["Read","Write","Edit","Bash"]

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"

Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

• Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python <path-to-scientific-schematics-skill>/scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add schematics:

• Paper transformation pipeline diagrams
• Website layout architecture diagrams
• Video production workflow illustrations
• Poster design process flowcharts
• Content extraction diagrams
• System architecture visualizations
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

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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

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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

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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

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Quick Start

Prerequisites

1. Install Paper2All:

   git clone https://github.com/YuhangChen1/Paper2All.git
   cd Paper2All
   conda create -n paper2all python=3.11
   conda activate paper2all
   pip install -r requirements.txt
   

2. Configure API Keys — export them; pipeline_all.py never calls load_dotenv(), so a .env file is ignored:

   export OPENROUTER_API_KEY=your_openrouter_api_key_here
   # Optional: GOOGLE_API_KEY and GOOGLE_CSE_ID for logo search
   

   OPENROUTER_API_KEY is the only key pipeline_all.py reads — its header says "Only supports OPENROUTER_API_KEY, does not support other APIs", and it returns immediately if the variable is unset. OPENAI_API_KEY has no effect.

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

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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 / Module Selection

[!warning] The CLI documentation in this skill diverges from the upstream Paper2All CLI — verify every command against the repo before use. In upstream pipeline_all.py, --model-choice selects the module to run, not the LLM: 1=Paper2Web, 2=Paper2Poster, 3=AutoPR (omit it to run all modules). There are no top-level --model_name_t/--model_name_v or --generate-website/-poster/-video arguments — the LLM is configured inside each module (e.g. Paper2Poster sets its own text/vision model). Do not read --model-choice values as GPT versions; note gpt-3.5-turbo is deprecated by OpenAI. pipeline_all.py declares --output_dir with an underscore while --input-dir uses a dash; upstream's README documents the dash form, which argparse rejects with exit 2.

• --model-choice 1: run Paper2Web (website)
• --model-choice 2: run Paper2Poster (poster)
• --model-choice 3: run AutoPR

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 configuration

The LLM is configured inside each upstream module (e.g. Paper2Poster sets its own text/vision model), not via a top-level flag in this skill's examples. To change models, configure them in the upstream Paper2All repo, and prefer a current model (gpt-3.5-turbo is deprecated by OpenAI).

Component Priority

For tight deadlines, generate in this order:

1. Website (fastest, most versatile, ~15-30 min)
2. Poster (moderate speed, for print deadlines, ~10-20 min)
3. Video (slowest, can be generated later, ~20-60 min)

Quality Assurance

Before finalizing outputs:

1. Website: Test on multiple devices, verify all links work, check figure quality
2. Poster: Print test page, verify text readability from 3-6 feet, check colors
3. Video: Watch entire video, verify audio synchronization, test on different devices

Resource Requirements

Processing Time

• Website: 15-30 minutes per paper
• Poster: 10-20 minutes per paper
• Video (no talking-head): 20-60 minutes per paper
• Video (with talking-head): 60-120 minutes per paper

Computational Requirements

• CPU: Multi-core processor for parallel processing
• RAM: 16GB minimum, 32GB recommended for large papers
• GPU: Optional for standard outputs, required for talking-head (NVIDIA A6000 48GB)
• Storage: 1-5GB per paper depending on components and quality settings

API Costs (Approximate)

• Website: $0.50-2.00 per paper (GPT-4)
• Poster: $0.30-1.00 per paper (GPT-4)
• Video: $1.00-3.00 per paper (GPT-4)
• Complete package: $2.00-6.00 per paper (GPT-4)

Troubleshooting

Common Issues

LaTeX parsing errors:

• Ensure LaTeX source compiles successfully: pdflatex main.tex
• Check all referenced files are present
• Verify no custom packages prevent parsing

Poor figure quality:

• Use vector formats (PDF, SVG, EPS) instead of rasters
• Ensure raster images are 300+ DPI
• Check figures render correctly in compiled PDF

Video generation failures:

• Verify sufficient disk space (5GB+ recommended)
• Check all dependencies installed (LibreOffice, Poppler)
• Review error logs in output directory

Poster layout issues:

• Verify poster dimensions are reasonable (24"-72" range)
• Check content length (very long papers may need manual curation)
• Ensure figures have appropriate resolution for poster size

API errors:

• Verify API keys in .env file
• Check API credit balance
• Ensure no rate limiting (wait and retry)

Platform-Specific Features

Social Media Optimization

The system auto-detects target platforms:

Twitter/X (English, numeric folder names):

mkdir -p input/001_twitter/
# Generates English promotional content

Xiaohongshu/小红书 (Chinese, alphanumeric folder names):

mkdir -p input/xhs_paper/
# Generates Chinese promotional content

Conference-Specific Formatting

Specify conference requirements:

• Standard poster sizes (4'×3', 5'×4', A0, A1)
• Video abstract length limits (typically 3-5 minutes)
• Institution branding requirements
• Color scheme preferences

Integration and Deployment

Website Deployment

Deploy generated websites to:

• GitHub Pages: Free hosting with custom domain
• Academic hosting: University web servers
• Personal servers: AWS, DigitalOcean, etc.
• Netlify/Vercel: Modern hosting with CI/CD

Poster Printing

Print-ready files work with:

• Professional poster printing services
• University print shops
• Online services (e.g., Spoonflower, VistaPrint)
• Large format printers (if available)

Video Distribution

Share videos on:

• YouTube: Public or unlisted for maximum reach
• Institutional repositories: University video platforms
• Conference platforms: Virtual conference systems
• Social media: Twitter, LinkedIn, ResearchGate

Advanced Usage

Batch Processing

Process multiple papers efficiently:

# Organize papers in batch directory
for paper in paper1 paper2 paper3; do
    python pipeline_all.py \
      --input-dir input/$paper \
      --output_dir output/$paper \
      --model-choice 1 &
done
wait

Custom Branding

Apply institution or lab branding:

• Provide logo files in paper directory
• Specify color schemes in configuration
• Use custom templates (advanced)
• Match conference theme requirements

Multi-Language Support

Generate content in different languages:

• Specify target language in configuration
• System translates content appropriately
• Selects appropriate voice for video narration
• Adapts design conventions to culture

References and Resources

This skill includes comprehensive reference documentation:

• references/installation.md: Complete installation and configuration guide
• references/paper2web.md: Detailed Paper2Web documentation with all features
• references/paper2video.md: Comprehensive Paper2Video guide including talking-head setup
• references/paper2poster.md: Complete Paper2Poster documentation with design templates
• references/usage_examples.md: Real-world examples and workflow patterns

External Resources:

• GitHub Repository: https://github.com/YuhangChen1/Paper2All
• Curated Dataset: Available on Hugging Face (13 research categories)
• Benchmark Suite: Reference websites and evaluation metrics

Evaluation and Quality Metrics

The Paper2All system includes built-in quality assessment:

Content Quality

• Completeness: Coverage of paper content
• Accuracy: Faithful representation of findings
• Clarity: Accessibility and understandability
• Informativeness: Key information prominence

Design Quality

• Aesthetics: Visual appeal and professionalism
• Layout: Balance, hierarchy, and organization
• Readability: Text legibility and figure clarity
• Consistency: Uniform styling and branding

Technical Quality

• Performance: Load times, responsiveness
• Compatibility: Cross-browser, cross-device support
• Accessibility: WCAG compliance, screen reader support
• Standards: Valid HTML/CSS, print-ready PDFs

All outputs undergo automated quality checks before generation completes.