前瞻强制：为自回归长视频扩散学习前瞻
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion
AI总结：
针对自回归长视频生成的有限预测范围导致视频质量难以维持的问题，提出Prospective Forcing框架，通过多头预测扩展监督范围，结合可学习嵌入与验证器，实现长程时间一致的视频生成并加速推理采样。
摘要：
自回归视频生成器通过分块滚动生成长视频，但通常仅被训练来预测紧邻的未来内容。这种有限的预测范围使得在长滚动过程中难以维持视频质量。为解决该问题，我们提出Prospective Forcing，这是一种多头预测框架，用于将长程预测信号注入自回归视频表示中。Prospective Forcing基于滚动窗口生成器构建，附加了轻量的、与偏移量相关的头，这些头共享相同的条件信息，并独立预测不同未来时间偏移量的分块。通过将监督范围扩展到下一个分块之外，这些头鼓励共享的历史表示保留对长程预测有用的信息，从而支持时间一致的生成。在推理时，我们通过使用已接受的未来分块预测来跳过全窗口骨干网络的执行，以加速采样。我们为该头配备了可学习的嵌入，用于生成下一个分块的多个候选，以及一个学习到的验证器，该验证器要么选择一个候选，要么拒绝所有候选并回退到标准自回归生成。
英文摘要：
Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future. This limited prediction horizon makes it challenging to maintain video quality over extended rollouts. To address this problem, we introduce Prospective Forcing, a multi-head prediction framework that injects long-range predictive signals into autoregressive video representations. Built on a rolling-window generator, Prospective Forcing attaches lightweight, offset-specific heads that share the same conditioning information and independently predict chunks at different future temporal offsets. By extending supervision beyond the next chunk, these heads encourage the shared history representation to retain information useful for longer-horizon prediction, supporting temporally consistent generation. At inference time, we accelerate sampling by using accepted future-chunk predictions to skip full-window backbone executions. We equip the head with learnable embeddings to generate multiple candidates for the next chunk, and a learned verifier either selects a candidate or rejects all and falls back to standard autoregressive generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
