视频AI终于能连续拍多镜头了
现在的视频AI一次只能干一件事：要么按文字生成，要么按参考图改，要么编辑已有素材，而且每次都是独立操作，没法记住前面拍过什么。这篇把三件事塞进一个模型，还让它能跨镜头保持连贯——你拍完第一镜，第二镜可以接着用第一镜的人物和场景，不用重新描述。关键是它把「记住历史」的成本压到固定预算，不会因为拍得越多越慢，单张GPU上能跑到每秒16帧。它不是你明天就能上手的工具，但这是视频AI从「单镜头玩具」走向「真能拍片」的关键一步。
📄 原文摘要(英文)
Recent video models increasingly support generation, reference conditioning, and editing within a single model, yet typically expose them as separate operations over fixed inputs. Practical creation unfolds across multiple shots, requiring one model to generate from text, follow a reference, or edit source footage while maintaining shared history. We formalize this setting as interactive multi-shot video creation (IMVC) and introduce ContextMaster, a unified model with a role-aware context representation for these operations. An interactive model must retain access to an expanding history without allowing the context read cost at each denoising step to grow. ContextMaster combines reusable clean context states with fixed budget sparse context routing and uses ConstraintSink to keep task constraints visible. To address the dual challenges of sparse context access and inference with few denoising steps, we propose a two-stage privileged context distillation framework, which transfers full context behavior from a dense teacher through consistency distillation and then refines deployment rollouts with distribution matching. Experiments on the three primitive tasks demonstrate improved task fulfillment and consistency across shots over specialized baselines. User studies further validate flexibly composed workflows, while the model reaches 16 FPS on a single GPU.