PipeFlow: Scalable Long-Form Video Editing with Pipelining and Motion Awareness
Published:Dec 30, 2025 06:54
•1 min read
•ArXiv
Analysis
This paper addresses the computational bottleneck of long-form video editing, a significant challenge in the field. The proposed PipeFlow method offers a practical solution by introducing pipelining, motion-aware frame selection, and interpolation. The key contribution is the ability to scale editing time linearly with video length, enabling the editing of potentially infinitely long videos. The performance improvements over existing methods (TokenFlow and DMT) are substantial, demonstrating the effectiveness of the proposed approach.
Key Takeaways
- •Proposes PipeFlow, a scalable video editing method for long-form videos.
- •Employs motion analysis to skip editing of low-motion frames.
- •Utilizes a pipelined task scheduling algorithm for parallel processing.
- •Leverages neural network-based interpolation for smooth transitions.
- •Achieves significant speedups compared to existing methods, enabling editing of potentially infinitely long videos.
Reference
“PipeFlow achieves up to a 9.6X speedup compared to TokenFlow and a 31.7X speedup over Diffusion Motion Transfer (DMT).”