上下文匹配蒸馏：自回归视频蒸馏中的教师因果性
原标题：Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
AI 摘要
Hugging Face 每日论文发布了一篇关于视频生成蒸馏的新研究，提出了上下文匹配蒸馏（CMD）框架，用于自回归视频模型。该方法通过将教师监督与因果生成上下文对齐，解决了现有视频DMD流程中教师评分依赖未来帧的问题，从而提升控制遵循度和长视频质量。实验表明，CMD在短视频和长视频基准上均达到最先进性能，并显著改善了对时变相机控制的遵循。
正文节选
Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation Abstract Context-Matched Distillation aligns teacher supervision with causal generation context for few-step autoregressive video models, improving control adherence and long-video quality. Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constr