Ruopeng Gao, Yukun Huang, Jiarui Hu, Shuai Wang, Wenbo Hu, Ying Shan, Limin Wang · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.29989 · cited by 0 works (OpenAlex) · open-access PDF
在教师强迫下,强有力的临时流动性可以使本地视觉连续性作为当前流动性消灭的捷径,减少对视觉进化模型的压力,但产生可信的延续性并不保证他们捕捉了场景如何演变。在教师强迫下,强有力的临时流动性可能允许本地视觉连续性作为流动性消灭的捷径,减少对视觉进化模型的压力。我们引入了未来流动性预测(FCP),一个辅助目标,与教师强迫消灭一起优化,预测时间分离的片段,而无需进入介入的视觉背景。直接从培训视频中提取监督,FCP不需要额外的预先训练的视觉编码器,并不会改变自我消灭的推断。我们的方法实现了比
Machine translation (Meta M2M100); the English original follows.
Autoregressive video diffusion models generate videos chunk by chunk, yet producing plausible continuations does not guarantee that they capture how scenes evolve. Under teacher forcing, strong temporal redundancy may allow local visual continuity to serve as a shortcut for current-chunk denoising, reducing the pressure to model visual evolution. We introduce Future Chunk Prediction (FCP), an auxiliary objective jointly optimized with teacher-forced denoising that predicts temporally separated chunks without access to the intervening visual context. Drawing supervision directly from training videos, FCP requires no additional pretrained visual encoder and leaves autoregressive inference unchanged. Our method achieves superior physical plausibility over standard teacher-forced autoregressive training with a lower overall training compute budget, highlighting the effectiveness and efficiency of future-chunk supervision for modeling visual evolution. Intriguingly, our visualizations suggest that FCP fosters a more structured representation space, which may be valuable for building autoregressive world models.
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Topics: Autoregressive model, Computer science, Redundancy (engineering), Inference, Artificial intelligence, Encoder
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