FlexiFilm: Long Video Generation with Flexible Conditions
<|reference_start|>FlexiFilm: Long Video Generation with Flexible Conditions: 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/<|reference_end|>
@article{ouyang2024flexifilm:,
    title={FlexiFilm: Long Video Generation with Flexible Conditions},
    author={Yichen Ouyang, jianhao Yuan, Hao Zhao, Gaoang Wang, Bo zhao},
    journal={arXiv preprint arXiv:2404.18620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18620},
    primaryClass={cs.CV}
}