Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models
<|reference_start|>Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models: With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusion models has shown great promise in controlling the shape and location of objects in the scene. Transferring image-editing techniques to the video domain, however, is extremely challenging as object motion and temporal consistency are difficult to capture accurately. In this work, we take a first look at the role of cross-attention in Text-to-Video (T2V) diffusion models for zero-shot video editing. While one-shot models have shown potential in controlling motion and camera movement, we demonstrate zero-shot control over object shape, position and movement in T2V models. We show that despite the limitations of current T2V models, cross-attention guidance can be a promising approach for editing videos.<|reference_end|>
@article{motamed2024investigating,
    title={Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot
  Editing of Text-to-Video Diffusion Models},
    author={Saman Motamed and Wouter Van Gansbeke and Luc Van Gool},
    journal={arXiv preprint arXiv:2404.05519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05519},
    primaryClass={cs.CV cs.LG}
}