Via: A Spatiotemporal Video Adaptation Framework for Global and Local Video Editing

🌈 Abstract

The paper introduces Via, a unified spatiotemporal video adaptation framework for consistent and precise video editing, pushing the limits of editing minute-long videos. The key contributions are:

• Test-time editing adaptation to improve semantic understanding and editing consistency within individual frames
• Local latent adaptation with automated mask generation for precise local control of editing targets across frames
• Spatiotemporal attention adaptation using a gather-and-swap strategy to maintain global editing consistency across frames

Through extensive experiments, the proposed Via framework demonstrates superior performance over existing techniques in both local edit precision and overall aesthetic quality of videos, enabling consistent editing of minute-long videos.

🙋 Q&A

[01] Test-Time Editing Adaptation for Consistent Local Editing

1. What are the two orthogonal approaches proposed for consistent local editing?

• Test-time fine-tuning to associate specific visual editing directions with the provided instructions, enhancing semantic consistency and edit quality
• Local latent adaptation with automated mask generation for precise editing, maintaining the integrity of non-targeted regions

2. How does the test-time fine-tuning process work?

• The image editing model first edits a randomly sampled frame with different random seeds, and the best editing result is chosen
• The tuning set is created by applying random affine transformations to the source and edited frames, along with the editing instruction
• Fine-tuning the image editing model on this domain-specific dataset helps the model associate specific visual editing directions with the provided instructions

3. How does the local latent adaptation work?

• A Large Vision-Language Model provides a textual description of the area to be edited, which is then used by the Segment Anything model to extract a mask
• During the diffusion process, the latent representation from the source frame is progressively blended with the target latent, using a linear interpolation to smoothly merge the source and target latents

[02] Spatiotemporal Adaptation for Consistent Global Editing

1. What is the two-step gather-swap process proposed for consistent global editing?

• In the group gathering stage, the model progressively edits the image using key and value from previous frames in the group, ensuring in-group consistency
• In the second stage, the model utilizes the attention group in the editing process of all frames, including the frames used to generate the attention group, ensuring maximum consistency between edited frames

2. How does the combination of self-attention and cross-attention improve the video editing quality?

• While self-attention is a standard practice for ensuring frame consistency, the authors found that incorporating cross-attention significantly enhances the video editing quality by better capturing the dynamic changes within the video

3. How are the attention variables selected to ensure broad representation of frame differences?

• To maximize coverage of the dynamic changes within a video, the attention variables are selected from frames that are evenly distributed throughout the video, ensuring a broad representation of frame differences.