Ruopeng Gao, Yukun Huang, Jiarui Hu, Shuai Wang, Wenbo Hu, Ying Shan, Limin Wang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29989 · open-access PDF
자율주행 비디오 확산 모델은 비디오를 조각별로 생성하지만, 믿을 수 있는 연속을 생성하면 장면이 어떻게 진화하는지 캡처할 수는 없습니다. 교사 강요하에 강력한 시간적 과도성은 현지 시각적 연속성을 현재-chunk denoising의 단계로 제공하여 시각적 진화 모델링에 대한 압력을 줄일 수있게 할 수 있습니다. 우리는 Future Chunk Prediction (FCP)를 도입합니다.
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 v
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