English

AnchorSync: Global Consistency Optimization for Long Video Editing

Computer Vision and Pattern Recognition 2025-08-21 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2508.14609,
  title  = {AnchorSync: Global Consistency Optimization for Long Video Editing},
  author = {Zichi Liu and Yinggui Wang and Tao Wei and Chao Ma},
  journal= {arXiv preprint arXiv:2508.14609},
  year   = {2025}
}

Comments

ACM MM 2025; Code is released at https://github.com/VISION-SJTU/AnchorSync

R2 v1 2026-07-01T04:58:18.445Z