Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models

Saman Motamed, Wouter Van Gansbeke, Luc Van Gool; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7406-7415

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.