HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising
Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a 1.8 wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.
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Kai Zou
claimed_verified
2026-03-11T16:31:50.635Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/647993d9f966f086918da59e/NDxz3PEpo3srZQNhwT7Qf.jpeg
Kai Zou
jackyhate
user
jackyhate
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Dian Zheng
claimed_verified
2026-03-10T09:44:51.839Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/YvPKZ_0gyJnvNwM1zK3JS.png
Dian Zheng
zhengli1013
user
zhengli1013
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Hongbo Liu
69af958fb3ab31dee1281d8c
Tiankai Hang
69af958fb3ab31dee1281d8d
Bin Liu
69af958fb3ab31dee1281d8e
Nenghai Yu
HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising
Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a 1.8 wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.
HiAR, a hierarchical autoregressive diffusion framework, improves video generation by conditioning on context at the same noise level and employs forward-KL regularization to maintain temporal continuity and motion diversity.
autoregressive diffusion temporal continuity error accumulation denoising bidirectional diffusion models hierarchical denoising framework causal generation pipelined parallel inference self-rollout distillation reverse-KL objective forward-KL regularizer bidirectional-attention mode VBench