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，没法记住前面拍过的内容。这篇论文把这三件事塞进同一个模型，让它能像导演一样连续拍多个镜头：先按文字生成第一镜，再拿第一镜当参考拍第二镜，还能中途改素材，全程共享上下文。关键是它用了一种“固定预算稀疏路由”的机制，让模型在每一步去噪时只读取一小部分历史信息，而不是把整个历史都塞进去，所以单张GPU上能跑到每秒16帧，实时可交互。它不是你明天就能用的产品，但这是视频AI从“单镜头工具”走向“能拍完整短片”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.