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Autoregressive video diffusion models face challenges like over-saturation and over-smoothing, limiting video realism. The paper proposes Mask Forcing, a dual-noise masking rollout technique to improve Distribution Matching Distillation and enhance visual quality.
Autoregressive video diffusion models face challenges like over-saturation and over-smoothing, limiting video realism. The paper proposes Mask Forcing, a dual-noise masking rollout technique to improve Distribution Matching Distillation and enhance visual quality.
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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...
Improving the realism and visual quality of autoregressive video diffusion models can advance real-time video generation applications. This method addresses key limitations in current distillation approaches, potentially enabling better video synthesis.
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