AnchorSync: Global Consistency Optimization for Long Video Editing
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 20 Aug 2025.
Builds on
33 of 52 references.
References · 33
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
- 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
- 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 961
- 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 927
- 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 670
21 more
- 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
- 2024Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teacherscited by 393
- 2023Pix2Video: Video Editing using Image Diffusioncited by 320
- 2023FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attentioncited by 313
- 2022Inversion-Based Style Transfer with Diffusion Modelscited by 291
- 2018Learning Blind Video Temporal Consistencycited by 253
- 2023Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translationcited by 253
- 2022Diffusion-based Image Translation using Disentangled Style and Content Representationcited by 149
- 2019Learning Data Augmentation Strategies for Object Detectioncited by 139
- 2024Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Controlcited by 117
- 2023VidToMe: Video Token Merging for Zero-Shot Video Editingcited by 113
- 2023FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesiscited by 79
- 2024ReVideo: Remake a Video with Motion and Content Controlcited by 74
- 2023DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphingcited by 67
- 2024I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Modelscited by 55
- 2024KeyVideoLLM: Towards Large-scale Video Keyframe Selectioncited by 54
- 2024Looking Backward: Streaming Video-to-Video Translation with Feature Bankscited by 38
- 2024DreamMover: Leveraging the Prior of Diffusion Models for Image Interpolation with Large Motioncited by 16
- 2024HybridBooth: Hybrid Prompt Inversion for Efficient Subject-Driven Generationcited by 6
- 2023Leveraging the Power of Data Augmentation for Transformer-based Trackingcited by 2
- 2024WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humanscited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.