ContextMaster：基于固定预算稀疏上下文路由的交互式多镜头视频创作
ContextMaster: Interactive Multi-Shot Video Creation via Fixed-Budget Sparse Context Routing
- Tsinghua University（清华大学）
- Nanjing University（南京大学）
- Kling Team, Kuaishou Technology（快手科技影团队）
- ShanghaiTech University（上海科技大学）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出统一模型ContextMaster，以固定预算稀疏上下文路由等技术解决交互式多镜头视频创作问题，在基础任务表现优于专用基线，单GPU达16 FPS且支持灵活工作流。
AI中文摘要：
近期的视频模型日益支持在单一模型内完成生成、参考条件控制及编辑操作，但通常将这些操作作为针对固定输入的独立操作呈现。实际创作需跨多个镜头展开，要求单一模型能从文本生成、遵循参考或编辑源素材，同时维持共享历史。我们将该场景形式化为交互式多镜头视频创作（IMVC），并引入ContextMaster，这是具备角色感知上下文表示的统一模型，用于上述操作。交互式模型必须能访问不断扩展的历史，且每一步去噪的上下文读取成本不得增长。ContextMaster结合可复用的干净上下文状态与固定预算稀疏上下文路由，并使用ConstraintSink保持任务约束可见。为解决稀疏上下文访问及少去噪步骤推理的双重挑战，我们提出两阶段特权上下文蒸馏框架，该框架通过一致性蒸馏从密集教师模型迁移完整上下文行为，再用分布匹配优化部署输出。对三个基础任务的实验表明，与专用基线相比，该模型在任务完成度及镜头间一致性上均有提升；用户研究进一步验证了灵活组合的工作流，且该模型在单GPU上达到16 FPS。
英文摘要：
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.
