| name | alterlab-paper-2-web |
| description | Converts academic papers into promotional and presentation formats — interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster) from LaTeX or PDF sources. Use when disseminating a paper, preparing for a conference, building an explorable academic homepage, generating a video abstract, or producing a print-ready poster from a paper. Part of the AlterLab Academic Skills suite. |
| allowed-tools | Read Write Edit Bash |
| license | MIT |
| compatibility | Requires an OPENAI_API_KEY (set in a .env file) for the generation pipelines; optional talking-head video needs an NVIDIA GPU (A6000 48GB) |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
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:
- Paper2Web: Interactive, explorable academic homepages with layout-aware design
- Paper2Video: Professional presentation videos with narration, slides, and optional talking-head
- 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
If a diagram or figure would aid comprehension, invoke the alterlab-scientific-schematics skill (diagrams/schematics) or the alterlab-generate-image skill (images). Figures are optional — add them only where they improve clarity.
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 layouts
- High-resolution, print-ready output
- QR codes / institution logos may need to be added manually (not documented pipeline flags)
Best For: Conference poster sessions, symposiums, academic exhibitions, virtual conferences
→ See references/paper2poster.md for detailed documentation
Quick Start
Prerequisites
-
Install Paper2All:
git clone https://github.com/YuhangChen1/Paper2All.git
cd Paper2All
conda create -n p2w python=3.11
conda activate p2w
pip install -r requirements.txt
Paper2Video runs in its own environment (conda create -n p2v python=3.10), and the optional talking-head module in a third (conda create -n hallo python=3.10).
-
Configure API Keys (create .env file):
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_API_BASE=https://api.openai.com/v1 # or https://openrouter.ai/api/v1 for OpenRouter
# Optional: GOOGLE_SEARCH_API_KEY and GOOGLE_SEARCH_ENGINE_ID for logo search
-
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
pipeline_all.py drives the website, poster, and PR-material modules. The --model-choice flag selects which component to run — it is NOT a model selector. Omit it to run all modules (with automatic PDF detection).
--model-choice | Component |
|---|
| (omitted) | All modules |
1 | Website (Paper2Web) only |
2 | Poster (Paper2Poster) only |
3 | PR materials (AutoPR) only |
Generate Everything (website + poster + PR materials):
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output"
Generate Website Only:
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output" \
--model-choice 1
Generate Poster with Custom Size:
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output" \
--model-choice 2 \
--poster-width-inches 60 \
--poster-height-inches 40
Generate Video (separate p2v environment, lightweight pipeline — no talking-head):
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"
For the full video pipeline with a Hallo2 talking-head, use pipeline.py with --ref_img, --ref_audio, and --gpu_list (requires the hallo env and an NVIDIA A6000 48GB GPU).
→ 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
Component Selection (pipeline_all.py)
--model-choice 1: Website (Paper2Web) only--model-choice 2: Poster (Paper2Poster) only--model-choice 3: PR materials (AutoPR) only- (omit
--model-choice): Run all modules
Model Selection (video pipeline)
--model_name_t: Model for text/script generation (e.g. gpt-4.1)--model_name_v: Model for visual/slide generation (e.g. gpt-4.1)
Poster Customization (pipeline_all.py)
--poster-width-inches [width]: Custom poster width--poster-height-inches [height]: Custom poster height
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
- Use LaTeX when possible: Provides best content extraction and structure
- Organize files properly: Keep all assets (figures, tables, bibliography) in paper directory
- High-quality figures: Use vector formats (PDF, SVG) or high-resolution rasters (300+ DPI)
- Clean LaTeX: Remove compilation artifacts, ensure source compiles successfully
Model Selection Strategy
The LLM is configured via OPENAI_API_KEY / OPENAI_API_BASE in .env (point the base at OpenRouter to use Claude or other models). The video pipeline takes explicit --model_name_t / --model_name_v overrides:
- Stronger models (e.g. gpt-4.1): Best for production-quality outputs, conferences, publications
- Cheaper/faster models: Fine for quick drafts, testing, or simple papers
Component Priority
For tight deadlines, generate in this order:
- Website (fastest, most versatile, ~15-30 min)
- Poster (moderate speed, for print deadlines, ~10-20 min)
- Video (slowest, can be generated later, ~20-60 min)
Quality Assurance
Before finalizing outputs:
- Website: Test on multiple devices, verify all links work, check figure quality
- Poster: Print test page, verify text readability from 3-6 feet, check colors
- 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/
Xiaohongshu/小红书 (Chinese, alphanumeric folder names):
mkdir -p input/xhs_paper/
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:
for paper in paper1 paper2 paper3; do
python pipeline_all.py \
--input-dir input/$paper \
--output-dir output/$paper &
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 guidereferences/paper2web.md: Detailed Paper2Web documentation with all featuresreferences/paper2video.md: Comprehensive Paper2Video guide including talking-head setupreferences/paper2poster.md: Complete Paper2Poster documentation with design templatesreferences/usage_examples.md: Real-world examples and workflow patterns
External Resources:
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.
