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English(EN) Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency

Cycle-World 框架解决了长视频生成中的误差累积问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为 Cycle-World 的新框架，旨在提高长时视频生成的稳定性和时间一致性。该方法通过在训练和推理过程中强制执行时间可逆性来解决自回归扩散模型中的误差累积问题。通过集成反向预测模型，Cycle-World 旨在限制生成漂移，并使用该模型作为运行时校正器来迭代地优化生成序列，从而显著减少视觉退化和结构崩溃。在 VBench 基准测试上的实验表明，Cycle-World 在生成高质量、时间一致的 60 秒视频方面取得了最先进的成果。 AI

影响 该框架可能带来更稳定、更逼真的长视频合成，影响依赖于高保真视频生成的领域。

排序理由 该集群包含一篇详细介绍视频生成新框架的学术论文。

在 arXiv cs.CV 阅读 →

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Cycle-World 框架解决了长视频生成中的误差累积问题

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该集群包含一篇详细介绍视频生成新框架的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Zihan Su, Teng Hu, Jiangning Zhang, Ruiyan Wang, Ran Yi, Lizhuang Ma, Dacheng Tao ·

    Cycle-World：通过反向预测周期一致性减轻长期视频世界模型中的误差累积

    arXiv:2607.11836v1 Announce Type: new Abstract: Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis, minor prediction deviations compound over time, ine…

  2. arXiv cs.CV TIER_1 English(EN) · Dacheng Tao ·

    Cycle-World：通过反向预测循环一致性缓解长期视频世界模型中的误差累积

    Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis, minor prediction deviations compound over time, inevitably leading to unconstrained generative drif…

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