AnchorSync：长视频编辑的全局一致性优化
AnchorSync: Global Consistency Optimization for Long Video Editing
AI总结：
针对长视频编辑中全局一致性与时间连贯性难以兼顾的问题，提出基于扩散的AnchorSync框架，通过解耦锚帧编辑与中间帧插值并结合渐进去噪和多模态引导，实现高质量长视频编辑，性能超越现有方法。
摘要：
长视频编辑由于需要在数千帧中同时保持全局一致性和时间连贯性，仍是一项具有挑战性的任务。现有方法在分钟级序列中常存在结构漂移或时间伪影问题。我们提出AnchorSync，一种基于扩散的新型框架，通过将任务解耦为稀疏锚帧编辑和平滑中间帧插值，实现高质量长视频编辑。该方法通过渐进式去噪过程强化结构一致性，通过多模态引导保留时间动态。大量实验表明，AnchorSync生成连贯、高保真的编辑结果，在视觉质量和时间稳定性上超越现有方法。
英文摘要：
Editing long videos remains a challenging task due to the need for maintaining both global consistency and temporal coherence across thousands of frames. Existing methods often suffer from structural drift or temporal artifacts, particularly in minute-long sequences. We introduce AnchorSync, a novel diffusion-based framework that enables high-quality, long-term video editing by decoupling the task into sparse anchor frame editing and smooth intermediate frame interpolation. Our approach enforces structural consistency through a progressive denoising process and preserves temporal dynamics via multimodal guidance. Extensive experiments show that AnchorSync produces coherent, high-fidelity edits, surpassing prior methods in visual quality and temporal stability.
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。
