Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.27203 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914605110919168 |
|---|---|
| author | Khurana, Mannat Jain, Sanyam Agarwal, Rishav |
| author_facet | Khurana, Mannat Jain, Sanyam Agarwal, Rishav |
| contents | Animation elevates digital documents into immersive experiences, yet creating custom motion paths remains cumbersome, requiring designers to manually select presets, plot Bézier points, and configure timing properties. We introduce Generative Animations, a system that transforms natural language prompts into production-ready animations. By chaining Large Language Models (LLMs) for semantic parsing with the Segment Anything Model (SAM) for visual grounding, our pipeline automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms. We demonstrate the system through three use cases: contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion on transformed objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27203 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Animations: A Multi-Model Pipeline for Prompt-Driven Motion Synthesis Khurana, Mannat Jain, Sanyam Agarwal, Rishav Computer Vision and Pattern Recognition Artificial Intelligence Animation elevates digital documents into immersive experiences, yet creating custom motion paths remains cumbersome, requiring designers to manually select presets, plot Bézier points, and configure timing properties. We introduce Generative Animations, a system that transforms natural language prompts into production-ready animations. By chaining Large Language Models (LLMs) for semantic parsing with the Segment Anything Model (SAM) for visual grounding, our pipeline automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms. We demonstrate the system through three use cases: contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion on transformed objects. |
| title | Generative Animations: A Multi-Model Pipeline for Prompt-Driven Motion Synthesis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.27203 |