Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a formidable challenge due to the compounded difficulties of motion modeling, semantic planning, and detail synthesis. To address these limitations, we propose \textbf{LUVE}, a \textbf{L}atent-cascaded \textbf{U}HR \textbf{V}ideo generation framework built upon dual frequency \textbf{E}xperts. LUVE employs a three-stage architecture comprising low-resolution motion generation for motion-consistent latent synthesis, video latent upsampling that performs resolution upsampling directly in the latent space to mitigate memory and computational overhead, and high-resolution content refinement that integrates low-frequency and high-frequency experts to jointly enhance semantic coherence and fine-grained detail generation. Extensive experiments demonstrate that our LUVE achieves superior photorealism and content fidelity in UHR video generation, and comprehensive ablation studies further validate the effectiveness of each component. The project is available at \href{https://unicornanrocinu.github.io/LUVE_web/}{https://github.io/LUVE/}.

翻译：近期视频扩散模型的进展显著提升了视觉质量，然而，由于运动建模、语义规划和细节合成的复合困难，超高分辨率视频生成仍然是一项艰巨挑战。为应对这些限制，我们提出了**LUVE**，一种基于双频专家的潜在级联超高分辨率视频生成框架。LUVE采用三阶段架构：包含用于运动一致潜在合成的低分辨率运动生成、直接在潜在空间执行分辨率上采样以减轻内存和计算开销的视频潜在上采样，以及集成低频与高频专家以共同增强语义连贯性和细粒度细节生成的高分辨率内容细化。大量实验表明，我们的LUVE在超高分辨率视频生成中实现了卓越的逼真度和内容保真度，全面的消融研究进一步验证了各组件的有效性。项目地址为 \href{https://unicornanrocinu.github.io/LUVE_web/}{https://github.io/LUVE/}。