Towards Controllable Video Synthesis of Routine and Rare OR Events
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arXiv
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| Natura: | Preprint |
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2026
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| author | Schneider, Dominik Seenivasan, Lalithkumar Rapuri, Sampath Anil, Vishalroshan Maksutova, Aiza Shen, Yiqing Mangulabnan, Jan Emily Ding, Hao Porras, Jose L. Ishii, Masaru Unberath, Mathias |
| author_facet | Schneider, Dominik Seenivasan, Lalithkumar Rapuri, Sampath Anil, Vishalroshan Maksutova, Aiza Shen, Yiqing Mangulabnan, Jan Emily Ding, Hao Porras, Jose L. Ishii, Masaru Unberath, Mathias |
| contents | Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare or safety-critical events in the OR.
Methods: This work presents an OR video diffusion framework that enables controlled synthesis of rare and safety-critical events. The framework integrates a geometric abstraction module, a conditioning module, and a fine-tuned diffusion model to first transform OR scenes into abstract geometric representations, then condition the synthesis process, and finally generate realistic OR event videos. Using this framework, we also curate a synthetic dataset to train and validate AI models for detecting near-misses of sterile-field violations.
Results: In synthesizing routine OR events, our method outperforms off-the-shelf video diffusion baselines, achieving lower FVD/LPIPS and higher SSIM/PSNR in both in- and out-of-domain datasets. Through qualitative results, we illustrate its ability for controlled video synthesis of counterfactual events. An AI model trained and validated on the generated synthetic data achieved a RECALL of 70.13% in detecting near safety-critical events. Finally, we conduct an ablation study to quantify performance gains from key design choices.
Conclusion: Our solution enables controlled synthesis of routine and rare OR events from abstract geometric representations. Beyond demonstrating its capability to generate rare and safety-critical scenarios, we show its potential to support the development of ambient intelligence models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21365 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Controllable Video Synthesis of Routine and Rare OR Events Schneider, Dominik Seenivasan, Lalithkumar Rapuri, Sampath Anil, Vishalroshan Maksutova, Aiza Shen, Yiqing Mangulabnan, Jan Emily Ding, Hao Porras, Jose L. Ishii, Masaru Unberath, Mathias Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare or safety-critical events in the OR. Methods: This work presents an OR video diffusion framework that enables controlled synthesis of rare and safety-critical events. The framework integrates a geometric abstraction module, a conditioning module, and a fine-tuned diffusion model to first transform OR scenes into abstract geometric representations, then condition the synthesis process, and finally generate realistic OR event videos. Using this framework, we also curate a synthetic dataset to train and validate AI models for detecting near-misses of sterile-field violations. Results: In synthesizing routine OR events, our method outperforms off-the-shelf video diffusion baselines, achieving lower FVD/LPIPS and higher SSIM/PSNR in both in- and out-of-domain datasets. Through qualitative results, we illustrate its ability for controlled video synthesis of counterfactual events. An AI model trained and validated on the generated synthetic data achieved a RECALL of 70.13% in detecting near safety-critical events. Finally, we conduct an ablation study to quantify performance gains from key design choices. Conclusion: Our solution enables controlled synthesis of routine and rare OR events from abstract geometric representations. Beyond demonstrating its capability to generate rare and safety-critical scenarios, we show its potential to support the development of ambient intelligence models. |
| title | Towards Controllable Video Synthesis of Routine and Rare OR Events |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2602.21365 |