End-to-End Training for Autoregressive Video Diffusion via Self-Resampling
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912771337093120 |
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| author | Guo, Yuwei Yang, Ceyuan He, Hao Zhao, Yang Wei, Meng Yang, Zhenheng Huang, Weilin Lin, Dahua |
| author_facet | Guo, Yuwei Yang, Ceyuan He, Hao Zhao, Yang Wei, Meng Yang, Zhenheng Huang, Weilin Lin, Dahua |
| contents | Autoregressive video diffusion models hold promise for world simulation but are vulnerable to exposure bias arising from the train-test mismatch. While recent works address this via post-training, they typically rely on a bidirectional teacher model or online discriminator. To achieve an end-to-end solution, we introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models from scratch and at scale. Central to our approach is a self-resampling scheme that simulates inference-time model errors on history frames during training. Conditioned on these degraded histories, a sparse causal mask enforces temporal causality while enabling parallel training with frame-level diffusion loss. To facilitate efficient long-horizon generation, we further introduce history routing, a parameter-free mechanism that dynamically retrieves the top-k most relevant history frames for each query. Experiments demonstrate that our approach achieves performance comparable to distillation-based baselines while exhibiting superior temporal consistency on longer videos owing to native-length training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End Training for Autoregressive Video Diffusion via Self-Resampling Guo, Yuwei Yang, Ceyuan He, Hao Zhao, Yang Wei, Meng Yang, Zhenheng Huang, Weilin Lin, Dahua Computer Vision and Pattern Recognition Autoregressive video diffusion models hold promise for world simulation but are vulnerable to exposure bias arising from the train-test mismatch. While recent works address this via post-training, they typically rely on a bidirectional teacher model or online discriminator. To achieve an end-to-end solution, we introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models from scratch and at scale. Central to our approach is a self-resampling scheme that simulates inference-time model errors on history frames during training. Conditioned on these degraded histories, a sparse causal mask enforces temporal causality while enabling parallel training with frame-level diffusion loss. To facilitate efficient long-horizon generation, we further introduce history routing, a parameter-free mechanism that dynamically retrieves the top-k most relevant history frames for each query. Experiments demonstrate that our approach achieves performance comparable to distillation-based baselines while exhibiting superior temporal consistency on longer videos owing to native-length training. |
| title | End-to-End Training for Autoregressive Video Diffusion via Self-Resampling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.15702 |