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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14306 |
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| _version_ | 1866914087284244480 |
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| author | P, Samarth Jain, Vyoman Golugula, Shiva Sathvik, Motamarri Sai |
| author_facet | P, Samarth Jain, Vyoman Golugula, Shiva Sathvik, Motamarri Sai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Manimator: Transforming Research Papers into Visual Explanations P, Samarth Jain, Vyoman Golugula, Shiva Sathvik, Motamarri Sai Artificial Intelligence Computers and Society Multimedia 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. |
| title | Manimator: Transforming Research Papers into Visual Explanations |
| topic | Artificial Intelligence Computers and Society Multimedia |
| url | https://arxiv.org/abs/2507.14306 |