Memory-V2V: Consistent Video Editing
An overview of Memory-V2V, a new framework that augments video-to-video diffusion models with explicit memory storage to ensure consistency across multi-turn video editing and long-timeline generation.Script
Have you ever tried to edit a video frame by frame, only to watch the character's appearance drift or vanish as the scene progresses? This paper tackles that specific 'amnesia' in generative video editing by giving diffusion models a dedicated visual memory.
Current video-to-video models excel at single-pass edits, but they struggle to maintain consistency across multiple iterative turns because they cannot efficiently process the entire editing history. The authors invoke a new strategy: effectively caching past outputs without overwhelming the model's context window.
Instead of feeding every past frame into the model, the system retrieves only the most relevant clips using a field-of-view overlap metric or visual similarity. It then uses dynamic tokenizers to allocate high detail to critical history while compressing less descriptive background elements.
This diagram illustrates the core pipeline, where the model selectively retrieves and tokenizes past video segments based on their relevance to the current frame. Notice how the adaptive merging mechanism reduces computational cost by over 30 percent without sacrificing visual fidelity.
Experiments show this method significantly outperforms baselines in cross-iteration consistency while reducing inference latency by nearly a third. However, the researchers note that complex scene transitions in multi-shot videos remain a challenge for the current architecture.
By treating video history as a searchable, compressible resource, this work moves us closer to stable, long-form generative video editing. For more deep dives into computer vision research, visit EmergentMind.com.
