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Anno Yanzhe Chen
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README.md
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README.md
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---
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## 🌟 Overview
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This repository contains the source code for the [Code2Video](https://chenanno.github.io/Code2Video/) Github.io website.
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<p align="center">
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<img src="figures/first.png" alt="Overview" width="90%">
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</p>
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**Code2Video** is an **agentic, code-centric framework** that generates high-quality **educational videos** from knowledge points.
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Unlike pixel-based text-to-video models, our approach leverages executable **Manim code** to ensure **clarity, coherence, and reproducibility**.
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**Key Features**:
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- 🎬 **Code-Centric Paradigm** — executable code as the unified medium for both temporal sequencing and spatial organization of educational videos.
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- 🤖 **Modular Tri-Agent Design** — Planner (storyboard expansion), Coder (debuggable code synthesis), and Critic (layout refinement with anchors) work together for structured generation.
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- 📚 **MMMC Benchmark** — the first benchmark for code-driven video generation, covering 117 curated learning topics inspired by 3Blue1Brown, spanning diverse areas.
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- 🧪 **Multi-Dimensional Evaluation** — systematic assessment on efficiency, aesthetics, and end-to-end knowledge transfer.
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---
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## 🚀 How to Create -- Code2Video
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<p align="center">
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<img src="figures/approach.png" alt="Approach" width="85%">
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</p>
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### 1. Requirements
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```bash
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pip install -r requirements.txt
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````
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### 2. Configure LLM API Keys
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Fill in your **API credentials** in `gpt_config.json`.
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* **LLM API**:
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* Required for Planner & Coder.
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* Best Manim code quality achieved with **Claude-4-Opus**.
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* **VLM API**:
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* Required for Planner Critic.
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* For layout and aesthetics optimization, provide **Gemini API key**.
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* Best quality achieved with **gemini-2.5-pro-preview-05-06**.
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* **Visual Assets API**:
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* To enrich videos with icons, set `ICONFINDER_API_KEY` from [IconFinder](https://www.iconfinder.com/account/applications).
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### 3. Run Agents
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We provide two shell scripts for different generation modes:
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#### (a) Full Benchmark Mode
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Script: `run_agent.sh`
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Runs all (or a subset of) learning topics defined in `long_video_topics_list.json`.
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```bash
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sh run_agent.sh
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```
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**Important parameters inside `run_agent.sh`:**
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* `API`: specify which LLM to use.
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* `FOLDER_PREFIX`: name prefix for saving output folders (e.g., `TEST-LIST`).
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* `MAX_CONCEPTS`: number of concepts to include (`-1` means all).
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* `PARALLEL_GROUP_NUM`: number of groups to run in parallel.
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---
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#### (b) Single Knowledge Point Mode
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Script: `run_agent_single.sh`
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Generates a video from a single **knowledge point** specified in the script.
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```bash
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sh run_agent_single.sh --knowledge_point "Linear transformations and matrices"
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```
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**Important parameters inside `run_agent_single.sh`:**
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* `API`: specify which LLM to use.
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* `FOLDER_PREFIX`: output folder prefix (e.g., `TEST-single`).
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* `KNOWLEDGE_POINT`: target concept, e.g. `"Linear transformations and matrices"`.
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---
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### 4. Project Organization
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A suggested directory structure:
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```
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Code2Video/
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│── agent.py
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│── run_agent.sh
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│── run_agent_single.sh
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│── api_config.json
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│── ...
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│
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├── assets/
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│ ├── icons/ # downloaded visual assets cache via IconFinder API
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│ └── reference/ # reference images
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│
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├── json_files/ # JSON-based topic lists & metadata
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├── prompts/ # prompt templates for LLM calls
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├── CASES/ # generated cases, organized by FOLDER_PREFIX
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│ └── TEST-LIST/ # example multi-topic generation results
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│ └── TEST-single/ # example single-topic generation results
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```
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---
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## 📊 How to Evaluate -- MMMC
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We evaluate along **three complementary dimensions**:
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1. **Knowledge Transfer (TeachQuiz)**
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```bash
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python3 eval_TQ.py
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```
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2. **Aesthetic & Structural Quality (AES)**
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```bash
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python3 eval_AES.py
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```
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3. **Efficiency Metrics (During Creating)**
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* Token usage
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* Execution time
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👉 More data and evaluation scripts are available at:
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[HuggingFace: MMMC Benchmark](https://huggingface.co/datasets/YanzheChen/MMMC)
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---
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## 🙏 Acknowledgements
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* Video data is sourced from the **[3Blue1Brown official lessons](https://www.3blue1brown.com/#lessons)**.
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These videos represent the **upper bound of clarity and aesthetics** in educational video design and inform our evaluation metrics.
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* We thank all the **Show Lab @ NUS** members for support!
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* This project builds upon open-source contributions from **Manim Community** and the broader AI research ecosystem.
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* High-quality visual assets (icons) are provided by **[IconFinder](https://www.iconfinder.com/)** and **[Icons8](https://icons8.com/icons)**, which were used to enrich the educational videos.
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---
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<!-- ## 📌 Citation
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If you find our work useful, please cite:
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```bibtex
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@article{chen2025code2video,
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title={Code2Video: Agentic Code-Centric Framework for Educational Video Generation},
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author={Chen, Yanzhe and Lin, Qinghong and Shou, Mike Zheng},
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journal={ICLR},
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year={2026}
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}
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```
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--- -->
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For more details and demos, please visit our [project page](https://github.com/showlab/Code2Video).
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