See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation
A new framework called OmniManim has been developed to improve the generation of educational animations from code. This framework addresses common visual defects in animations by incorporating visual planning and structured diagnostics. The study shows that OmniManim significantly enhances render quality compared to existing methods.
- ▪OmniManim is designed to generate executable code for educational animations while minimizing visual defects.
- ▪The framework includes a Vision Agent that optimizes keyframe layouts and reduces animation failures.
- ▪Two datasets, ManimLayout-1K and EduRequire-500, were created to evaluate the framework's performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15585 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
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Computer Science > Artificial Intelligence arXiv:2605.15585 (cs) [Submitted on 15 May 2026] Title:See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation Authors:Yuejia Li, Ke He, Junheng Li, Shutong Chen, Jingkang Xia, Zhiyue Su, Junchi Zhang, Mang Ye View a PDF of the paper titled See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation, by Yuejia Li and 7 other authors View PDF HTML (experimental) Abstract:Large language models can generate executable code for educational animations, but the resulting renders often exhibit visual defects, including element overlap, misalignment, and broken animation continuity.
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