Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917766409224192 |
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| author | Chen, Qihua Ma, Yue Wang, Hongfa Yuan, Junkun Zhao, Wenzhe Tian, Qi Wang, Hongmei Min, Shaobo Chen, Qifeng Liu, Wei |
| author_facet | Chen, Qihua Ma, Yue Wang, Hongfa Yuan, Junkun Zhao, Wenzhe Tian, Qi Wang, Hongmei Min, Shaobo Chen, Qifeng Liu, Wei |
| contents | This paper explores higher-resolution video outpainting with extensive content generation. We point out common issues faced by existing methods when attempting to largely outpaint videos: the generation of low-quality content and limitations imposed by GPU memory. To address these challenges, we propose a diffusion-based method called \textit{Follow-Your-Canvas}. It builds upon two core designs. First, instead of employing the common practice of "single-shot" outpainting, we distribute the task across spatial windows and seamlessly merge them. It allows us to outpaint videos of any size and resolution without being constrained by GPU memory. Second, the source video and its relative positional relation are injected into the generation process of each window. It makes the generated spatial layout within each window harmonize with the source video. Coupling with these two designs enables us to generate higher-resolution outpainting videos with rich content while keeping spatial and temporal consistency. Follow-Your-Canvas excels in large-scale video outpainting, e.g., from 512X512 to 1152X2048 (9X), while producing high-quality and aesthetically pleasing results. It achieves the best quantitative results across various resolution and scale setups. The code is released on https://github.com/mayuelala/FollowYourCanvas |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation Chen, Qihua Ma, Yue Wang, Hongfa Yuan, Junkun Zhao, Wenzhe Tian, Qi Wang, Hongmei Min, Shaobo Chen, Qifeng Liu, Wei Computer Vision and Pattern Recognition This paper explores higher-resolution video outpainting with extensive content generation. We point out common issues faced by existing methods when attempting to largely outpaint videos: the generation of low-quality content and limitations imposed by GPU memory. To address these challenges, we propose a diffusion-based method called \textit{Follow-Your-Canvas}. It builds upon two core designs. First, instead of employing the common practice of "single-shot" outpainting, we distribute the task across spatial windows and seamlessly merge them. It allows us to outpaint videos of any size and resolution without being constrained by GPU memory. Second, the source video and its relative positional relation are injected into the generation process of each window. It makes the generated spatial layout within each window harmonize with the source video. Coupling with these two designs enables us to generate higher-resolution outpainting videos with rich content while keeping spatial and temporal consistency. Follow-Your-Canvas excels in large-scale video outpainting, e.g., from 512X512 to 1152X2048 (9X), while producing high-quality and aesthetically pleasing results. It achieves the best quantitative results across various resolution and scale setups. The code is released on https://github.com/mayuelala/FollowYourCanvas |
| title | Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.01055 |