SOURCE-LINKED INTELLIGENCE
Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout
Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smoothing issues, resulting in limited visual quality and realism. The key contributing factor is the mode-seeking behavior of the reverse KL objective in DMD, which can cause the student distribution to collapse onto only a few modes of the teacher distribution. To address this, we pr
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T17:50:05.000Z
First collected: 2026-09-20T20:02:11.508Z. This is not the publication date.