Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion
To address this problem, we introduce Prospective Forcing, a multi-head prediction framework that injects long-range predictive signals into autoregressive video representations.
ProofPaper ↗
Key points
- Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.
- 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.
- 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.
Sources (1)
- [1]Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video DiffusionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:07 AM
To address this problem, we introduce Prospective Forcing, a multi-head prediction framework that injects long-range predictive signals into autoregressive video representations.
Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.
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