Code2Video introduces a revolutionary framework for generating professional educational videos directly from executable Python code. Unlike pixel-based diffusion or text-to-video models, Code2Video treats code as the core generative medium, enabling precise visual control, transparency, and interpretability in long-form educational content.
Developed by Show Lab (National University of Singapore), the system coordinates three collaborative agents, namely: Planner, Coder, and Critic to produce structured videos that are scalable.
Key Highlights
- Code-Centric Generation: Uses Manim-based executable code as a transparent, controllable substrate for video creation.
- Tri-Agent Architecture:
- Planner: Designs temporally coherent lecture flow.
- Coder: Generates executable code with automatic debugging.
- Critic: Employs visual anchor prompts & multimodal feedback for spatial refinement.
- TeachQuiz Metric: A novel evaluation that tests real knowledge transfer by “unlearning” and “relearning” concepts through generated videos.
- MMMC Benchmark: Built from 3Blue1Brown-style Manim tutorials across 13 subjects, evaluating aesthetics, efficiency, and educational efficacy.
- 40% Performance Boost: Outperforms direct code generation baselines and achieves learning outcomes comparable to human-made tutorials.
Why It Matters
Code2Video pioneers a new generation of agentic, interpretable video generation systems, transforming code into a bridge between logic and learning. By fusing programmatic precision with multimodal feedback, it establishes a foundation for autonomous, verifiable, and scalable educational content creation, from math and physics tutorials to AI and coding lectures.
Explore More
- Paper: arXiv:2510.01174
- Project Page: https://showlab.github.io/Code2Video/
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