跳到主要内容
AI News HubLIVE
来源内容 · 翻译待补全2 分钟阅读

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

文章摘要

AI 服务暂时不可用,以下为来源摘要,待恢复后补全翻译: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…

来源arXiv Computer Vision作者: Yuqian Zhou, Zhenghong Zhou, Zongze Wu, Cameron Smith, Richard Zhang, Jiebo Luo, Eli Shechtman, Zhe Lin
待翻译:EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing
报告错误

纠错通道尚未开通,可先复制下方文章信息留存。

查看更正说明
直接读正文

AI 服务暂时不可用,以下为来源正文,待恢复后补全翻译。

--> [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?)

展开要点与分析

文章情报

工程师进阶

要点

  • AI 服务暂时不可用,系统已先保留来源内容与降级元数据。
  • 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…

要点与分析由自动化流程生成,可能有误,请结合原始来源核实。