FlexiFilm: Long Video Generation with Flexible Conditions
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913333753413632 |
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| author | Ouyang, Yichen Yuan, jianhao Zhao, Hao Wang, Gaoang zhao, Bo |
| author_facet | Ouyang, Yichen Yuan, jianhao Zhao, Hao Wang, Gaoang zhao, Bo |
| contents | Generating long and consistent videos has emerged as a significant yet challenging problem. While most existing diffusion-based video generation models, derived from image generation models, demonstrate promising performance in generating short videos, their simple conditioning mechanism and sampling strategy-originally designed for image generation-cause severe performance degradation when adapted to long video generation. This results in prominent temporal inconsistency and overexposure. Thus, in this work, we introduce FlexiFilm, a new diffusion model tailored for long video generation. Our framework incorporates a temporal conditioner to establish a more consistent relationship between generation and multi-modal conditions, and a resampling strategy to tackle overexposure. Empirical results demonstrate FlexiFilm generates long and consistent videos, each over 30 seconds in length, outperforming competitors in qualitative and quantitative analyses. Project page: https://y-ichen.github.io/FlexiFilm-Page/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_18620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlexiFilm: Long Video Generation with Flexible Conditions Ouyang, Yichen Yuan, jianhao Zhao, Hao Wang, Gaoang zhao, Bo Computer Vision and Pattern Recognition Generating long and consistent videos has emerged as a significant yet challenging problem. While most existing diffusion-based video generation models, derived from image generation models, demonstrate promising performance in generating short videos, their simple conditioning mechanism and sampling strategy-originally designed for image generation-cause severe performance degradation when adapted to long video generation. This results in prominent temporal inconsistency and overexposure. Thus, in this work, we introduce FlexiFilm, a new diffusion model tailored for long video generation. Our framework incorporates a temporal conditioner to establish a more consistent relationship between generation and multi-modal conditions, and a resampling strategy to tackle overexposure. Empirical results demonstrate FlexiFilm generates long and consistent videos, each over 30 seconds in length, outperforming competitors in qualitative and quantitative analyses. Project page: https://y-ichen.github.io/FlexiFilm-Page/ |
| title | FlexiFilm: Long Video Generation with Flexible Conditions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.18620 |