Video Token Merging for Computational Cost Reduction
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Solution Overview
Problem
Existing video processing technologies face challenges in efficiently modifying video content while preserving temporal consistency, often requiring high computational costs due to diffusion inversion and cross-frame interactions.
Innovation Solution
The proposed system employs a spatio-temporal token merging mechanism and saliency-based merging to fuse redundant tokens, reducing computational costs by avoiding unnecessary diffusion processes in background regions and adaptively allocating resources based on foreground and background regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If diffusion inversion and cross-frame interactions are applied to modify video content, then modification quality and temporal consistency are improved, but computational costs increase significantly
Solution Approach 1:
The patent segments video frames into foreground regions and background regions based on saliency detection. Different processing strategies are applied to each region: the diffusion process is applied to foreground regions for high-quality modification, while background regions are processed more efficiently without full diffusion inversion, thus reducing overall computational costs while maintaining modification quality where needed.
Solution Approach 2:
The patent implements local quality by applying the computationally intensive diffusion inversion process selectively only to foreground regions that require modification, while using lighter processing for background regions. This ensures high modification quality in salient areas while significantly reducing global computational burden.
2Manufacturing precision
If diffusion processes are applied to all frames and regions, then modification quality is maintained, but processing time increases
Solution Approach 1:
The patent applies partial action by performing full diffusion inversion only on foreground regions rather than all frames and regions. Background regions are processed with reduced computational intensity, achieving acceptable modification quality for those regions while dramatically improving overall processing speed through selective application of the diffusion process.
3Reliability
If all tokens are processed independently, then processing completeness is ensured, but computational efficiency decreases
Solution Approach 1:
The patent merges redundant tokens by detecting salient regions and combining their processing requirements. Instead of processing each token independently, salient tokens in foreground regions are grouped together for unified diffusion processing, while background tokens are processed collectively with reduced intensity, thereby improving computational efficiency while maintaining processing completeness through systematic region-based handling.
Data Source
AI summary
Systems and techniques are described herein for modifying video data. For instance, a method for modifying video data is provided. The method may include obtaining first tokens based on a first frame of video data, wherein each of the first tokens comprises a feature vector corresponding to a respective location within the first frame of video data; obtaining second tokens based on a second frame of video data, wherein each of the second tokens comprises a feature vector corresponding to a respective location within the second frame of video data; determining a destination token from among the first tokens; determining candidate tokens from among the second tokens based on respective relationships between the candidate tokens and the destination token; merging the candidate tokens with the destination token resulting in modified second tokens; and processing the modified second tokens using a diffusion model.