Paper2Video

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Paper2Video: Automatic Video Generation from Scientific Papers
从学术论文自动生成演讲视频

Zeyu Zhu*, Kevin Qinghong Lin*, Mike Zheng Shou
Show Lab, National University of Singapore

📄 Paper | 🤗 Daily Paper | 📊 Dataset | 🌐 Project Website | 💬 X (Twitter)

• Input: a paper ➕ an image ➕ an audio

Paper	Image	Audio
🔗 Paper link	Hinton's photo	🔗 Audio sample

• Output: a presentation video

hinton.2.mp4

Check out more examples at 🌐 project page.

🔥 Update

Any contributions are welcome!

• [2025.10.15] We update a new version without talking-head for fast generation!
• [2025.10.11] Our work receives attention on YC Hacker News.
• [2025.10.9] Thanks AK for sharing our work on Twitter!
• [2025.10.9] Our work is reported by Medium.
• [2025.10.8] Check out our demo video below!
• [2025.10.7] We release the arxiv paper.
• [2025.10.6] We release the code and dataset.
• [2025.9.28] Paper2Video has been accepted to the Scaling Environments for Agents Workshop(SEA) at NeurIPS 2025.

d35df30ad813f1cf53eccab0ad525b5d.mp4

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Table of Contents

• 🌟 Overview
• 🚀 Quick Start: PaperTalker
  • 1. Requirements
  • 2. Configure LLMs
  • 3. Inference
• 📊 Evaluation: Paper2Video
• 😼 Fun: Paper2Video for Paper2Video
• 🙏 Acknowledgements
• 📌 Citation

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🌟 Overview

This work solves two core problems for academic presentations:

• Left: How to create a presentation video from a paper?
  PaperTalker — an agent that integrates slides, subtitling, cursor grounding, speech synthesis, and talking-head video rendering.

• Right: How to evaluate a presentation video?
  Paper2Video — a benchmark with well-designed metrics to evaluate presentation quality.

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🚀 Try PaperTalker for your Paper!

1. Requirements

Prepare the environment:

cd src
conda create -n p2v python=3.10
conda activate p2v
pip install -r requirements.txt
conda install -c conda-forge tectonic

[Optional] Skip this part if you do not need a human presenter.

Download the dependent code and follow the instructions in Hallo2 to download the model weight.

git clone https://github.com/fudan-generative-vision/hallo2.git

You need to prepare the environment separately for talking-head generation to potential avoide package conflicts, please refer to Hallo2. After installing, use which python to get the python environment path.

cd hallo2
conda create -n hallo python=3.10
conda activate hallo
pip install -r requirements.txt

2. Configure LLMs

Export your API credentials:

export GEMINI_API_KEY="your_gemini_key_here"export OPENAI_API_KEY="your_openai_key_here"

The best practice is to use GPT4.1 or Gemini2.5-Pro for both LLM and VLMs. We also support locally deployed open-source model(e.g., Qwen), details please referring to Paper2Poster.

3. Inference

The script pipeline.py provides an automated pipeline for generating academic presentation videos. It takes LaTeX paper sources together with reference image/audio as input, and goes through multiple sub-modules (Slides → Subtitles → Speech → Cursor → Talking Head) to produce a complete presentation video. ⚡ The minimum recommended GPU for running this pipeline is NVIDIA A6000 with 48G.

Example Usage

Run the following command to launch a fast generation (without talking-head generation):

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/latex_proj \
--ref_img /path/to/ref_img.png \
--ref_audio /path/to/ref_audio.wav \
--gpu_list [0,1,2,3,4,5,6,7]

Run the following command to launch a full generation (with talking-head generation):

python pipeline.py \
--model_name_t gpt-4.1 \
--model_name_v gpt-4.1 \
--model_name_talking hallo2 \
--result_dir /path/to/output \
--paper_latex_root /path/to/latex_proj \
--ref_img /path/to/ref_img.png \
--ref_audio /path/to/ref_audio.wav \
--talking_head_env /path/to/hallo2_env \
--gpu_list [0,1,2,3,4,5,6,7]

Argument	Type	Default	Description
--model_name_t	str	gpt-4.1	LLM
--model_name_v	str	gpt-4.1	VLM
--model_name_talking	str	hallo2	Talking Head model. Currently only hallo2 is supported
--result_dir	str	/path/to/output	Output directory (slides, subtitles, videos, etc.)
--paper_latex_root	str	/path/to/latex_proj	Root directory of the LaTeX paper project
--ref_img	str	/path/to/ref_img.png	Reference image (must be square portrait)
--ref_audio	str	/path/to/ref_audio.wav	Reference audio (recommended: ~10s)
--ref_text	str	None	Optional reference text (for style guidance for subtitles)
--beamer_templete_prompt	str	None	Optional reference text (for style guidance for slides)
--gpu_list	list[int]	""	GPU list for parallel execution (used in cursor generation and Talking Head rendering)
--if_tree_search	bool	True	Whether to enable tree search for slide layout refinement
--stage	str	"[0]"	Pipeline stages to run (e.g., [0] full pipeline, [1,2,3] partial stages)
--talking_head_env	str	/path/to/hallo2_env	python environment path for talking-head generation

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📊 Evaluation: Paper2Video

Unlike natural video generation, academic presentation videos serve a highly specialized role: they are not merely about visual fidelity but about communicating scholarship. This makes it difficult to directly apply conventional metrics from video synthesis(e.g., FVD, IS, or CLIP-based similarity). Instead, their value lies in how well they disseminate research and amplify scholarly visibility.From this perspective, we argue that a high-quality academic presentation video should be judged along two complementary dimensions:

For the Audience

• The video is expected to faithfully convey the paper’s core ideas.
• It should remain accessible to diverse audiences.

For the Author

• The video should foreground the authors’ intellectual contribution and identity.
• It should enhance the work’s visibility and impact.

To capture these goals, we introduce evaluation metrics specifically designed for academic presentation videos: Meta Similarity, PresentArena, PresentQuiz, IP Memory.

Run Eval

• Prepare the environment:

cd src/evaluation
conda create -n p2v_e python=3.10
conda activate p2v_e
pip install -r requirements.txt

• For MetaSimilarity and PresentArena:

python MetaSim_audio.py --r /path/to/result_dir --g /path/to/gt_dir --s /path/to/save_dir
python MetaSim_content.py --r /path/to/result_dir --g /path/to/gt_dir --s /path/to/save_dir

python PresentArena.py --r /path/to/result_dir --g /path/to/gt_dir --s /path/to/save_dir

• For PresentQuiz, first generate questions from paper and eval using Gemini:

cd PresentQuiz
python create_paper_questions.py ----paper_folder /path/to/data
python PresentQuiz.py --r /path/to/result_dir --g /path/to/gt_dir --s /path/to/save_dir

• For IP Memory, first generate question pairs from generated videos and eval using Gemini:

cd IPMemory
python construct.py
python ip_qa.py

See the codes for more details!

👉 Paper2Video Benchmark is available at: HuggingFace

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😼 Fun: Paper2Video for Paper2Video

Check out How Paper2Video for Paper2Video:

output.mp4

🙏 Acknowledgements

• The souces of the presentation videos are SlideLive and YouTuBe.
• We thank all the authors who spend a great effort to create presentation videos!
• We thank CAMEL for open-source well-organized multi-agent framework codebase.
• We thank the authors of Hallo2 and Paper2Poster for their open-sourced codes.
• We thank Wei Jia for his effort in collecting the data and implementing the baselines. We also thank all the participants involved in the human studies.
• We thank all the Show Lab @ NUS members for support!

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📌 Citation

If you find our work useful, please cite:

@misc{paper2video,
title={Paper2Video: Automatic Video Generation from Scientific Papers}, author={Zeyu Zhu and Kevin Qinghong Lin and Mike Zheng Shou},
year={2025},
eprint={2510.05096},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.05096}, }