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+ Logo + Code2Video: A Code-centric Paradigm +
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+ for Educational Video Generation +
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+ + Yanzhe Chen* +   + + Kevin Qinghong Lin* +   + + Mike Zheng Shou✉ + +

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+ + Show + Lab, National University of Singapore + +
+ * Equal Contribution  + ✉ Corresponding Author +

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+ + Code + + + arXiv + + + PDF + +

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+ Showcase of Code2Video +

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The videos below are generated via Coding.

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Abstract

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+ 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. +

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Method

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+ 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. +

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