DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing
<|reference_start|>DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing: Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing approaches, here we focus on score distillation sampling to circumvent the standard reverse diffusion process and initiate optimization from videos that already exhibit natural motion. Our analysis reveals that while video score distillation can effectively introduce new content indicated by target text, it can also cause significant structure and motion deviation. To counteract this, we propose to match space-time self-similarities of the original video and the edited video during the score distillation. Thanks to the use of score distillation, our approach is model-agnostic, which can be applied for both cascaded and non-cascaded video diffusion frameworks. Through extensive comparisons with leading methods, our approach demonstrates its superiority in altering appearances while accurately preserving the original structure and motion.<|reference_end|>
@article{jeong2024dreammotion:,
    title={DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot
  Video Editing},
    author={Hyeonho Jeong, Jinho Chang, Geon Yeong Park, Jong Chul Ye},
    journal={arXiv preprint arXiv:2403.12002},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12002},
    primaryClass={cs.CV cs.AI}
}