SurGen: Text-Guided Diffusion Model for Surgical Video Generation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914956601982976 |
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| author | Cho, Joseph Schmidgall, Samuel Zakka, Cyril Mathur, Mrudang Kaur, Dhamanpreet Shad, Rohan Hiesinger, William |
| author_facet | Cho, Joseph Schmidgall, Samuel Zakka, Cyril Mathur, Mrudang Kaur, Dhamanpreet Shad, Rohan Hiesinger, William |
| contents | Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis. SurGen produces videos with the highest resolution and longest duration among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14028 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SurGen: Text-Guided Diffusion Model for Surgical Video Generation Cho, Joseph Schmidgall, Samuel Zakka, Cyril Mathur, Mrudang Kaur, Dhamanpreet Shad, Rohan Hiesinger, William Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis. SurGen produces videos with the highest resolution and longest duration among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees. |
| title | SurGen: Text-Guided Diffusion Model for Surgical Video Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2408.14028 |