MLV-Edit: 向分钟级视频编辑的致一致性和高效率迈进
MLV-Edit: Towards Consistent and Highly Efficient Editing for Minute-Level Videos
AI总结：
MLV-Edit通过分段编辑和两个核心模块提升分钟级视频编辑的稳定性和效率
摘要：
我们提出了MLV-Edit，一个无需训练的基于流的框架，旨在解决分钟级视频编辑的特殊挑战。尽管现有技术在短视频处理方面表现优异，但将其扩展到长时长视频仍然具有挑战性，因为计算开销过大且难以在成千上万的帧中保持全局时间一致性。为此，MLV-Edit采用分段编辑的分治策略，通过两个核心模块实现：速度混合通过对齐相邻块的流场来校正段边界处的运动不一致，消除碎片视频处理中常见的闪烁和边界伪影；注意力沉降将局部段特征锚定到全局参考帧，有效抑制累积的结构漂移。大量定量和定性实验表明，MLV-Edit在时间稳定性和语义保真度方面始终优于最先进的方法。
英文摘要：
We propose MLV-Edit, a training-free, flow-based framework that address the unique challenges of minute-level video editing. While existing techniques excel in short-form video manipulation, scaling them to long-duration videos remains challenging due to prohibitive computational overhead and the difficulty of maintaining global temporal consistency across thousands of frames. To address this, MLV-Edit employs a divide-and-conquer strategy for segment-wise editing, facilitated by two core modules: Velocity Blend rectifies motion inconsistencies at segment boundaries by aligning the flow fields of adjacent chunks, eliminating flickering and boundary artifacts commonly observed in fragmented video processing; and Attention Sink anchors local segment features to global reference frames, effectively suppressing cumulative structural drift. Extensive quantitative and qualitative experiments demonstrate that MLV-Edit consistently outperforms state-of-the-art methods in terms of temporal stability and semantic fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
