- - Showcase of Code2Video -
-The videos below are generated via Coding.
- -Hanoi Problem
-Puzzles
-Neural Network Structure
-Neural Networks
-History and Definition of π
-Calculus
-Space-filling Curves
-Topology
-Abstract
- - -- While recent generative models advance pixel-space video synthesis, they remain limited in - producing - professional ecu videos, which demand disciplinary knowledge, precise visual structures, and - coherent transitions, limiting their applicabilityl in educational scenarios. Intuitively, such - requirements are better addressed through the manipulation of a renderable environment, which - can be - explicitly controlled via logical commands (e.g., code). In this work, we propose - Code2Video, a - codecentric agent framework for generating educational videos via executable Python code. The - framework - comprises three collaborative agents: (i) Planner, which structures lecture content into - temporally - coherent flows and prepares corresponding visual assets; (ii) Coder, which converts - structured - instructions into executable Python codes while incorporating scope-guided auto-fix to enhance - efficiency; and (iii) Critic, which leverages vision-language models (VLM) with anchor - visual - prompts to refine spatial layout and ensure clarity. To support systemati evaluation, we build - MMMC, - a benchmark of professionally produced, long-form, disciplinespecific educational videos. We - evaluate - MMMC across diverse dimensions, including VLM-as-a-Judge aesthetic scores, code efficiency, and - particularly, TeachQuiz, a novel end-to-end knowledge transfer measured by a VLM's - ability to - learn - from the generated videos. Our results demonstrate the potential of Code2Video as a scalable, - interpretable, and controllable approach for educational video generation. -
-Method
- - -- Illustration of Code2Video. Given a user inquiry, Code2Video aims to render an - educational - video - via Manim code writing: (i) the Planner converts a learning topic into a storyboard and - retrieves - visual assets; (ii) the Coder performs parallel code synthesis with scope-guided refinement to - ensure efficiency and temporal consistency; (iii) the Critic uses anchor visual prompts to - iteratively adjust spatial layout and clarity, yielding reproducible, pedagogically structured - videos. -
- -
-
-
-