Manimator: Transforming Research Papers into Visual Explanations

TL;DR

The paper addresses the challenge of teaching complex STEM concepts from dense literature by introducing manimator, an open-source, LLM-driven pipeline that converts prompts or research papers into visual explanations rendered with Manim. It presents a three-stage workflow—scene description generation, Manim code generation, and animation rendering—facilitated by stage-specific prompts and model selection to balance quality and cost. Through TheoremExplainBench and human evaluations, manimator demonstrates strong performance in visual relevance, logical flow, and overall quality, suggesting the approach can scale to a wide range of topics beyond theorems. The work aims to democratize the creation of dynamic, pedagogically effective explanations, with public-access tools and an emphasis on accessibility for educators, students, and researchers alike.

Abstract

Understanding complex scientific and mathematical concepts, particularly those presented in dense research papers, poses a significant challenge for learners. Dynamic visualizations can greatly enhance comprehension, but creating them manually is time-consuming and requires specialized knowledge and skills. We introduce manimator, an open-source system that leverages Large Language Models to transform research papers and natural language prompts into explanatory animations using the Manim engine. Manimator employs a pipeline where an LLM interprets the input text or research paper PDF to generate a structured scene description outlining key concepts, mathematical formulas, and visual elements and another LLM translates this description into executable Manim Python code. We discuss its potential as an educational tool for rapidly creating engaging visual explanations for complex STEM topics, democratizing the creation of high-quality educational content.

Figures (4)

• Figure 1: The Manimator Workflow: User provides input via a natural language prompt, PDF upload, or arXiv ID. The pipeline has three stages: (1) an LLM generates a structured Markdown scene description from input using prompts and examples; (2) a code-focused LLM converts this into Manim Python code; (3) the code is rendered into an animation using the Manim engine.
• Figure 2: Combined system prompts used for Stage 1 (Scene Description Generation) and Stage 2 (Manim Code Generation).
• Figure 3: Examples of animations generated by Manimator.
• Figure 4: Interface used for collecting human evaluation ratings on generated animations.