Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation
Autoregressive video diffusion models generate videos chunk by chunk, yet producing plausible continuations does not guarantee that they capture how scenes evolve.
ProofPaper ↗
Key points
- 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.
Sources (2)
- [1]Looking Ahead: Modeling Future Evolution in Autoregressive Video GenerationHugging Face Daily Papers · Oct 21, 12:00 AM
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.
- [2]Looking Ahead: Modeling Future Evolution in Autoregressive Video GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:28 PM
Extractive summary: sentences quoted from the sources.
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