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The paper introduces LUVE, a latent-cascaded framework for ultra-high-resolution (UHR) video generation that tackles challenges in motion modeling, semantic planning, and detail synthesis. LUVE uses a three-stage architecture: low-resolution motion generation, latent upsampling, and high-resolution content refinement with dual frequency experts. Experiments demonstrate that LUVE achieves superior photorealism and content fidelity in UHR video generation compared to existing methods.
Generate ultra-high-resolution videos with photorealism and fidelity using a novel latent-cascaded approach that cleverly balances computational cost and detail.
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/}.
