跳到主要內容
AI News HubLIVE
來源內容 · 翻譯待補全2 分鐘閱讀

待翻譯:EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing

文章摘要

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.21424v1 Announce Type: new Abstract: Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified framework for interactive video generation and editing. EditStream unifies multiple video creation and manipulation tasks within a single DiT-based model through flexible task-specific conditioning, and further transforms it into a fast, few-step autoregressive model for efficient streaming. It supports Text-to-Video, Image-to-Video, Video-to-Video, Editing Propagation, Reference-guided Video Editing, and Camera Pose Change, enabling flexible control over video generation, transformation, and editing within one system. To make the unified model practical for interacti…

來源arXiv Computer Vision作者: Yuqian Zhou, Zhenghong Zhou, Zongze Wu, Cameron Smith, Richard Zhang, Jiebo Luo, Eli Shechtman, Zhe Lin
待翻譯:EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing
回報錯誤

更正管道尚未開通,可先複製下方文章資訊留存。

查看更正說明
直接讀正文

AI 服務暫時不可用,以下為來源正文,待恢復後補全翻譯。

--> [Submitted on 16 Aug 2026] Title:EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing View a PDF of the paper titled EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing, by Yuqian Zhou and 7 other authors View PDF HTML (experimental) Abstract:Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified framework for interactive video generation and editing. EditStream unifies multiple video creation and manipulation tasks within a single DiT-based model through flexible task-specific conditioning, and further transforms it into a fast, few-step autoregressive model for efficient streaming. It supports Text-to-Video, Image-to-Video, Video-to-Video, Editing Propagation, Reference-guided Video Editing, and Camera Pose Change, enabling flexible control over video generation, transformation, and editing within one system. To make the unified model practical for interactive use, we develop a two-stage distillation approach that combines Velocity Moment Matching (VMM) with autoregressive unrolling. VMM matches conditional velocity moments at student-reached intermediate states to preserve generation quality and motion, while unrolling exposes the student to its own autoregressive predictions to improve temporal stability. Together, they alleviate common challenges in few-step autoregressive video generation, including over-saturation, degraded motion, temporal instability, and complex training. EditStream provides a practical and scalable solution that bridges high-quality diffusion-based video models with interactive creative workflows. Comments: 25 pages, 12 figures, Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multimedia (cs.MM) Cite as: arXiv:2608.21424 [cs.CV] (or arXiv:2608.21424v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.21424 arXiv-issued DOI via DataCite Submission history From: Yuqian Zhou [view email] [v1] Sun, 16 Aug 2026 07:27:18 UTC (17,352 KB) Full-text links: Access Paper: View a PDF of the paper titled EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing, by Yuqian Zhou and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.GR cs.HC cs.LG cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

展開要點與分析

文章情報

工程師進階

要點

  • AI 服務暫時不可用,系統已先保留來源內容與降級後設資料。
  • arXiv:2608.21424v1 Announce Type: new Abstract: Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce Edit…

技術影響

可能影響 Agent 架構、工具呼叫、工作流自動化和產品整合。

要點與分析由自動化流程生成,可能有誤,請結合原始來源核實。