Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation
Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse: under the same prompt, different noise samples tend to produce highly similar videos with weakened motion dynamics. We analyze this degradation in Distribution Matching Distillation (DMD)-distilled AR video generators and find that, in the autoregressive setting, it takes the form of a structured uncertainty collapse: the mode-seeking bias of DMD maps different noise samples to nearly identical first chunks, and the deterministic AR cache then propagates this collapsed state to all subsequent chunks, turning a local loss of stochasticity at the rollout root into a gl...
| Authors | Zixuan Duan, Xunzhi Xiang, Yabo Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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