TheoremExplainAgent: Generate 5+ minute animated math explainer videos with Manim

General Introduction

TheoremExplainAgent is an innovative project developed by TIGER AI Lab to transform complex math and science theorems into easy-to-understand video animations using artificial intelligence technology. The tool is based on the reasoning power of Large Language Models (LLMs), combined with animation generation and speech synthesis technologies, to automate the generation of theorem explaining videos of more than 5 minutes to help users intuitively grasp academic concepts. The project is open-sourced on GitHub and has received attention from the education and technology communities for students, teachers, and learners interested in STEM (Science, Technology, Engineering, and Mathematics) fields.TheoremExplainAgent's core goal is to make abstract theorems lively and interesting by enhancing learning through a multimodal approach.

Manim is a Python library for creating high-quality math animations. It can be used to create visualizations of mathematical concepts, geometric transformations, images of functions, physics simulations, and more, and is perfect for teaching, presentations, and science videos (such as those on the 3Blue1Brown channel). The project currently has no released code, and last year there was a tool with a similar principle called Gatekeep It can be used as a reference.

Function List

• Automatic generation of theorem videos: Enter the content of the theorem and the system automatically generates a complete video containing animation, voice and text explanations.
• Multi-modal output support: Combines text-based reasoning, animated visualizations, and audio narration to provide a three-dimensional learning experience.
• dual intelligent body architecture (DIBA): Utilizing two AI intelligences working in tandem, one to reason about theorems and one to generate video content.
• Interdisciplinary coverage: Supports theorem interpreting in multiple fields such as mathematics, physics, chemistry, and computer science.
• Open source code and datasets: Full code and related resources are provided, allowing for user customization or secondary development.
• High-quality content output: Mimic the human video production process to ensure that the generated videos are logical and fluent.

Using Help

TheoremExplainAgent is an open source project based on GitHub, and users need a certain technical foundation to install and use it. The following is a detailed guide to help you quickly get started with this tool.

Installation process (not open)

1. environmental preparation
   • Make sure you have Python 3.8 or later installed on your computer.
   • Install Git for cloning project code from GitHub.
   • It is recommended to use a virtual environment to avoid dependency conflicts with the following command:

     python -m venv venv
     source venv/bin/activate # Linux/Mac
     venv\Scripts\activate # Windows
     

2. Cloning Project Code
   • Open a terminal and enter the following command to download the project from GitHub:

     git clone https://github.com/TIGER-AI-Lab/TheoremExplainAgent.git
     cd TheoremExplainAgent
     

3. Installation of dependencies
   • Project dependencies include large language modeling libraries, animation generation tools such as Manim, and speech synthesis modules. Run the following command to install all dependencies:

     pip install -r requirements.txt
     

   • in the event that requirements.txt is not provided, refer to the libraries mentioned in the project documentation, e.g. transformers,manim cap (a poem) gTTS, manual installation.
4. Configuration models and tools
   • Download the pre-trained large language model (e.g., LLaMA or GPT variant) and configure its path to the project configuration file.
   • Ensure that Manim is installed correctly and run the following command to test it:

     manim-v
     

   • If you need speech functionality, install a speech synthesis tool (such as Google Text-to-Speech) and configure the API key.
5. Verify Installation
   • Run the sample script provided with the project to check if the video is generated properly:

     python examples/run_demo.py
     

Main Functions

Generation Theorem Explained Video

• Step 1: Preparing the theorem input
  In the project root directory, find the input folder (if not, create it manually), create a text file (such as the theorem.txt), writing theorems that need to be explained, for example:

Pythagorean Theorem: In a right triangle, the square of the hypotenuse equals the sum of the squares of the other two sides.

• Step 2: Run the Generation Script
  Use the command line to run the main script, specifying the input file:

python generate_video.py --input theorem.txt --output video.mp4

• Step 3: View Results
  The generated video will be saved in the specified path (e.g. video.mp4), contains animation and voice narration.

Customizing the content of the theorem

• compiler configs/config.yaml file, adjusting parameters such as video duration, animation style, or speech rate. Example:

  video.
  duration: 300 # video duration (seconds)
  style: "simple" # Animation style
  voice.
  speed: 1.0 # Speech speed
  

• Rerun the Generate command to see the customization effect.

Debugging and Optimization

• If the video generation fails, check the log file (usually in the logs/ folder) to troubleshoot the problem. Common problems include wrong model paths or missing dependency libraries.
• Adjusting the inference parameters of the LLM (e.g. temperature values) temperature) to improve the logic of generated content:

  python generate_video.py --input theorem.txt --temperature 0.7
  

Featured Function Operation

Multimodal Output Experience

• Animation Generation: Based on the Manim engine, the system breaks down theorems into visualization steps. For example, the collinearity theorem generates a dynamic presentation of triangles and square areas.
• audio commentary: The voice module generates a natural language narration based on the reasoning, automatically synchronized with the animation.
• Text Support: Subtitles are embedded in the video for the convenience of hard-of-hearing users.

Interdisciplinary theorem support

• When theorems from different disciplines are entered, the system automatically adapts the explanation to the content. For example, a physics theorem might generate a motion trajectory animation, and a computer science theorem might show an algorithmic flowchart.
• Example Input:

  Newton's Second Law: Force equals mass times acceleration.
  

  The generated results will contain animated demonstrations of force, mass and acceleration.

Tips for use

• batch file: Write multiple theorems to a single file, separated by newlines, and the script will generate videos one by one.
• Community Support: If you encounter problems, you can submit feedback on the GitHub Issues page, and the TIGER AI Lab team and community will help.
• secondary development: Users familiar with Python can modify the generate_video.py, add new features such as support for more languages or animation effects.

With the above steps, you can easily use TheoremExplainAgent to generate high-quality theorem explaining videos, which can dramatically increase the efficiency and fun of both self-study and teaching.