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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.07619 |
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| _version_ | 1866929588963115008 |
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| author | Li, Linyuan Qiu, Jianing Saha, Anujit Li, Lin Li, Poyuan He, Mengxian Guo, Ziyu Yuan, Wu |
| author_facet | Li, Linyuan Qiu, Jianing Saha, Anujit Li, Lin Li, Poyuan He, Mengxian Guo, Ziyu Yuan, Wu |
| contents | As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Biomedical Video Generation Li, Linyuan Qiu, Jianing Saha, Anujit Li, Lin Li, Poyuan He, Mengxian Guo, Ziyu Yuan, Wu Computer Vision and Pattern Recognition As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation |
| title | Artificial Intelligence for Biomedical Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.07619 |