Context-Aware Input Orchestration for Video Inpainting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913818255294464 |
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| author | Kim, Hoyoung Khudoyberdiev, Azimbek Jeong, Seonghwan Ryoo, Jihoon |
| author_facet | Kim, Hoyoung Khudoyberdiev, Azimbek Jeong, Seonghwan Ryoo, Jihoon |
| contents | Traditional neural network-driven inpainting methods struggle to deliver high-quality results within the constraints of mobile device processing power and memory. Our research introduces an innovative approach to optimize memory usage by altering the composition of input data. Typically, video inpainting relies on a predetermined set of input frames, such as neighboring and reference frames, often limited to five-frame sets. Our focus is to examine how varying the proportion of these input frames impacts the quality of the inpainted video. By dynamically adjusting the input frame composition based on optical flow and changes of the mask, we have observed an improvement in various contents including rapid visual context changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16926 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Context-Aware Input Orchestration for Video Inpainting Kim, Hoyoung Khudoyberdiev, Azimbek Jeong, Seonghwan Ryoo, Jihoon Computer Vision and Pattern Recognition Traditional neural network-driven inpainting methods struggle to deliver high-quality results within the constraints of mobile device processing power and memory. Our research introduces an innovative approach to optimize memory usage by altering the composition of input data. Typically, video inpainting relies on a predetermined set of input frames, such as neighboring and reference frames, often limited to five-frame sets. Our focus is to examine how varying the proportion of these input frames impacts the quality of the inpainted video. By dynamically adjusting the input frame composition based on optical flow and changes of the mask, we have observed an improvement in various contents including rapid visual context changes. |
| title | Context-Aware Input Orchestration for Video Inpainting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16926 |