AnchorSync: Global Consistency Optimization for Long Video Editing

• URL: http://arxiv.org/abs/2508.14609v1
• Date: Wed, 20 Aug 2025 10:51:24 GMT
• Title: AnchorSync: Global Consistency Optimization for Long Video Editing
• Authors: Zichi Liu, Yinggui Wang, Tao Wei, Chao Ma,
• Abstract summary: We introduce AnchorSync, a novel diffusion-based framework that enables high-quality, long-term video editing.<n>Our approach enforces structural consistency through a progressive denoising process and preserves temporal dynamics via multimodal guidance.
• Score: 8.65329684912554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Zero-Shot Video Translation and Editing with Frame Spatial-Temporal Correspondence [81.82643953694485]
  We present FRESCO, which integrates intra-frame correspondence with inter-frame correspondence to formulate a more robust spatial-temporal constraint.<n>Our method goes beyond attention guidance to explicitly optimize features, achieving high spatial-temporal consistency with the input video.<n>We verify FRESCO adaptations on two zero-shot tasks of video-to-video translation and text-guided video editing.
  arXiv  Detail & Related papers  (2025-12-03T15:51:11Z)
• STCDiT: Spatio-Temporally Consistent Diffusion Transformer for High-Quality Video Super-Resolution [60.06664986365803]
  We present STCDiT, a video super-resolution framework built upon a pre-trained video diffusion model.<n>It aims to restore structurally faithful and temporally stable videos from degraded inputs, even under complex camera motions.
  arXiv  Detail & Related papers  (2025-11-24T05:37:23Z)
• Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled Sampling [81.37449968164692]
  We propose Synchronized Coupled Sampling (SynCoS), a novel inference framework that synchronizes denoising paths across the entire video.<n>Our approach combines two complementary sampling strategies, which ensure seamless local transitions and enforce global coherence.<n>Extensive experiments show that SynCoS significantly improves multi-event long video generation, achieving smoother transitions and superior long-range coherence.
  arXiv  Detail & Related papers  (2025-03-11T16:43:45Z)
• Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion [116.40704026922671]
  First-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation.<n>We propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency.
  arXiv  Detail & Related papers  (2025-01-15T18:59:15Z)
• Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion [22.988212617368095]
  We propose GLC-Diffusion, a tuning-free method for long video generation.<n>It models the long video denoising process by establishing Global-Local Collaborative Denoising.<n>We also propose a Video Motion Consistency Refinement (VMCR) module that computes the gradient of pixel-wise and frequency-wise losses.
  arXiv  Detail & Related papers  (2025-01-08T05:49:39Z)
• Enhancing Long Video Generation Consistency without Tuning [92.1714656167712]
  We address issues to enhance the consistency and coherence of videos generated with either single or multiple prompts.<n>We propose the Time-frequency based temporal Attention Reweighting Algorithm (TiARA), which judiciously edits the attention score matrix.<n>For videos generated by multiple prompts, we further uncover key factors such as the alignment of the prompts affecting prompt quality.<n>Inspired by our analyses, we propose PromptBlend, an advanced prompt pipeline that systematically aligns the prompts.
  arXiv  Detail & Related papers  (2024-12-23T03:56:27Z)
• Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior [13.595032265551184]
  Video-to-video synthesis poses significant challenges in maintaining character consistency, smooth temporal transitions, and preserving visual quality during fast motion. We propose an adaptive motion-guided cross-frame attention mechanism that selectively reduces redundant computations. This enables a greater number of cross-frame attentions over more frames within the same computational budget.
  arXiv  Detail & Related papers  (2024-06-07T12:12:25Z)
• VidToMe: Video Token Merging for Zero-Shot Video Editing [100.79999871424931]
  We propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. Our method improves temporal coherence and reduces memory consumption in self-attention computations.
  arXiv  Detail & Related papers  (2023-12-17T09:05:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.