流强制：构建用于鲁棒流视频生成的统一训练轨迹

中文摘要 AI 辅助

流视频生成在世界建模中具有巨大潜力，需在线顺序推断未来帧以形成连续视频流。然而，流视频扩散模型存在根本的训练-推理不匹配问题：推理遵循专门的去噪顺序，而高级训练策略通常需要多样化的噪声水平配置。为解决训练-推理一致性与训练覆盖度之间的权衡，我们将视频扩散采样重新表述为噪声水平上的帧索引随机过程。在该随机过程空间中，我们构建了连续训练轨迹，采样计划沿此轨迹从独立采样逐步演变为与推理一致的采样。我们进一步引入联合校准算法和时间相关采样算法，以确保轨迹平滑性和跨帧相关性。基于这些设计，我们提出了Stream Forcing，一种用于流视频生成的统一训练框架，平衡训练充分性和推理效率。大量实验表明，Stream Forcing在UCF-101基准测试上将FVD指标提升了36.6%，显著提高了生成质量；此外，该方法还能实现对长时序视频生成的鲁棒零样本泛化，在UCF-101基准测试上将FVD指标提升了27.9%。

英文摘要

Streaming video generation holds strong potential for world modeling, where future frames must be inferred online sequentially to form a continuous video stream. However, streaming video diffusion models introduce a fundamental train-inference mismatch: inference follows a specialized denoising order, whereas advanced training strategies typically require diverse noise-level configurations. To address this trade-off between train-inference consistency and training coverage, we reformulate the video diffusion sampling as a frame-indexed stochastic process over noise levels. Within this stochastic process space, we construct a continuous training trajectory along which the sampling schedule progressively evolves from independent sampling to inference-consistent sampling. We further introduce a joint calibration algorithm and a temporal correlative sampling algorithm to ensure trajectory smoothness and cross-frame correlation. Building on these designs, we propose Stream Forcing, a unified training framework for streaming video generation that balances training sufficiency and inference efficiency. Extensive experiments demonstrate that Stream Forcing significantly improves generation quality with a 36.6% FVD improvement on the UCF-101 benchmark. Furthermore, our method facilitates robust zero-shot extrapolation to long-horizon video generation with a 27.9% FVD improvement on the UCF-101 benchmark.

发表机构

• Huazhong University of Science & Technology（华中科技大学）
• Horizon Robotics（地平线机器人）
• Anyverse Dynamics

机构由 AI 辅助整理，请以论文原文为准。

补充信息

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15 pages,6 figures