End-to-End Training for Autoregressive Video Diffusion via Self-Resampling
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.
69437cdb542d62d58a7bf766
Yuwei Guo
69437cdb542d62d58a7bf767
Ceyuan Yang
69437cdb542d62d58a7bf768
Hao He
69437cdb542d62d58a7bf769
Yang Zhao
69437cdb542d62d58a7bf76a
Meng Wei
69437cdb542d62d58a7bf76b
Zhenheng Yang
69437cdb542d62d58a7bf76c
Weilin Huang
69437cdb542d62d58a7bf76d
Dahua Lin
End-to-End Training for Autoregressive Video Diffusion via Self-Resampling
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.
Resampling Forcing is introduced as a teacher-free framework to train autoregressive video diffusion models with improved temporal consistency using self-resampling and history routing.
autoregressive video diffusion models exposure bias train-test mismatch Resampling Forcing self-resampling scheme sparse causal mask frame-level diffusion loss history routing temporal consistency