EditaLive! Unified Character Video Editing for Live Streaming

  • 类型：arxiv
  • 标识：2608.27123
  • 链接：https://arxiv.org/abs/2608.27123
  • 主分类：multimodal
  • 形态：method
  • 被引：0
  • 被引来源：Semantic Scholar + OpenAlex
  • S2被引：0
  • OpenAlex被引：0
  • 影响力被引：0
  • TLDR：Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically depend on multiple offline inference steps, making them unsuitable for real-time interaction. We propose EditaLive, a novel framework for real-time streaming character video editing. In detail, we start from a pretrained image animation model (Wan-Animate), which naturally decoupl
  • OpenAlex ID：W7204535623
  • OpenAlex DOI：10.48550/arxiv.2608.27123
  • DOI：10.48550/arxiv.2608.27123
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://arxiv.org/pdf/2608.27123
  • OpenAlex更新：2026-09-02
  • 待LLM分类：否
  • 标题中文：EditaLive! 面向直播的统一人物视频编辑
  • TLDR中文：传统视频编辑主要关注场景级内容，而直播更强调人物主体。然而，直接将现有视频编辑方法应用于以人为中心的直播仍具挑战，因为它们可能引入面部表情不一致，且通常依赖多个离线推理步骤，难以满足实时交互需求。我们提出 EditaLive，一个用于实时流式角色视频编辑的新型框架。具体而言，我们从预训练图像动画模型（Wan-Animate）出发，该模型天然解耦...
  • 来源文件：
  • /inbox/tom/_candidates/2026-08-29-agent-rag-longcontext-candidates.json
  • /inbox/tom/_candidates/2026-08-30-agent-rag-longcontext-candidates.json
  • [S2 enrich]
  • /inbox/tom/_candidates/2026-08-31-agent-rag-longcontext-candidates.json
  • /inbox/tom/_candidates/2026-08-31-agent-memory-tool-use-candidates.json
  • [OpenAlex backfill]