Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912103193903104 |
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| author | Wang, Zhenbin Zhang, Lei Wang, Lituan Zhu, Minjuan Zhang, Zhenwei |
| author_facet | Wang, Zhenbin Zhang, Lei Wang, Lituan Zhu, Minjuan Zhang, Zhenwei |
| contents | Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, and simulation. Current video diffusion models typically build on image diffusion architecture by incorporating temporal operations (such as 3D convolution and temporal attention). Although this approach is effective, its oversimplification limits spatio-temporal performance and consumes substantial computational resources. To counter this, we propose Medical Simulation Video Generator (MedSora), which incorporates three key elements: i) a video diffusion framework integrates the advantages of attention and Mamba, balancing low computational load with high-quality video generation, ii) an optical flow representation alignment method that implicitly enhances attention to inter-frame pixels, and iii) a video variational autoencoder (VAE) with frequency compensation addresses the information loss of medical features that occurs when transforming pixel space into latent features and then back to pixel frames. Extensive experiments and applications demonstrate that MedSora exhibits superior visual quality in generating medical videos, outperforming the most advanced baseline methods. Further results and code are available at https://wongzbb.github.io/MedSora |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_01647 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation Wang, Zhenbin Zhang, Lei Wang, Lituan Zhu, Minjuan Zhang, Zhenwei Computer Vision and Pattern Recognition Artificial Intelligence Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, and simulation. Current video diffusion models typically build on image diffusion architecture by incorporating temporal operations (such as 3D convolution and temporal attention). Although this approach is effective, its oversimplification limits spatio-temporal performance and consumes substantial computational resources. To counter this, we propose Medical Simulation Video Generator (MedSora), which incorporates three key elements: i) a video diffusion framework integrates the advantages of attention and Mamba, balancing low computational load with high-quality video generation, ii) an optical flow representation alignment method that implicitly enhances attention to inter-frame pixels, and iii) a video variational autoencoder (VAE) with frequency compensation addresses the information loss of medical features that occurs when transforming pixel space into latent features and then back to pixel frames. Extensive experiments and applications demonstrate that MedSora exhibits superior visual quality in generating medical videos, outperforming the most advanced baseline methods. Further results and code are available at https://wongzbb.github.io/MedSora |
| title | Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.01647 |