ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion
Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the next video chunk, updating all patches with the same denoising schedule and number of function evaluations. This ignores the spatial heterogeneity of video content: static, simple, or recurring regions are often easier to denoise, while other regions require further refinement or additional context. A uniform budget thus leads to redundant computation on easy patches, whereas reducing the fixed step count can leave difficult patches insufficiently refined....
| Authors | Zekai Gu, Peng Wang, Jiahao Lu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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- 16 Oct 2026 Description: Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the nex → Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the nex
- 16 Oct 2026 Topics added cs.CV removed hf-daily-papers, autoregressive video diffusion models, denoising steps, spatial heterogeneity, routing module, few-step diffusion
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