See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation
Quick Take
OmniManim enhances educational animation generation by integrating visual priors for improved render quality.
Key Points
- Introduces render-feedback-aware code generation.
- Utilizes a Vision Agent for visual planning.
- Achieves higher render quality on EduRequire-500.
📖 Reader Mode
~2 min readAbstract: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. These defects cannot be reliably detected from the code alone and become apparent only after execution. We formalize this problem as render-feedback-aware constrained code generation: given a natural language specification, the model must generate executable code whose rendered output satisfies structured quality criteria that can be evaluated only after rendering. To address this problem, we introduce OmniManim, a render-feedback-aware educational animation generation framework built around a shared scene state, explicit visual planning, structured post-render diagnostics, and localized repair. Within OmniManim, the Vision Agent is a task-specific visual planning module: it predicts sparse keyframe layouts with coarse-to-fine bounding-box denoising and optimizes an interpolation-aware objective to reduce intermediate-frame failures induced by downstream animation interpolation. We further construct two datasets, ManimLayout-1K and EduRequire-500, and provide a reproducible evaluation protocol covering executability, instructional quality, visual quality, and efficiency. On EduRequire-500, OmniManim improves measured render quality over both single-model baselines and existing multi-agent frameworks. Systematic ablation studies further verify that explicit visual planning, especially its coarse spatial prior, bounding-box refinement, and interpolation-aware optimization, is central to these gains.
| Comments: | 21 pages, 4 figures |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.15585 [cs.AI] |
| (or arXiv:2605.15585v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15585 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuejia Li [view email]
[v1]
Fri, 15 May 2026 03:48:26 UTC (3,510 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →From Prompts to Protocols: An AI Agent for Laboratory Automation
An AI agent integrates large language models for automating laboratory protocols, enhancing efficiency and accuracy.