AtlasVid: 通过解耦的全局-局部建模实现高效超高清长视频生成
AtlasVid: Efficient Ultra-High-Resolution Long Video Generation via Decoupled Global-Local Modeling
AI总结：
本文提出AtlasVid框架，通过解耦建模提升超高清长视频生成效率，实现60.9倍加速和更低训练成本，优于原生4K生成器。
摘要：
近期基于扩散的视频生成器在视觉保真度和提示可控性方面取得了显著进展，但将其扩展到超高清（UHR）长视频仍极具挑战性。难点尤其体现在长单次生成中，需保持连续场景的全局时间一致性，同时不依赖剪辑过渡或自回归镜头拼接的精细空间细节。本文从解耦建模角度重新审视这一挑战。我们主张现有视频扩散模型已编码了强局部视觉先验，而主要瓶颈在于如何高效扩展全局时空建模以适应更高的分辨率和持续时间。基于此见解，我们提出AtlasVid，一种解耦的全局-局部框架，用于高效UHR长视频生成。AtlasVid首先通过时间缩放RoPE生成低分辨率和低FPS的全局语义代理，从而扩展时间范围而不增加训练token数量。在该代理的引导下，高分辨率细节分支进行联合去噪，采用分层局部性保持注意力。重新排列的时空窗口保持几何局部性，不对称的全局-局部注意力注入对齐的语义指导并保留模型的预训练能力。此设计使模型具备分辨率无关的训练能力：模型仅在720P上训练，使用轻量LoRA适配，即可直接泛化到4K及更长（>10秒）的视频生成。实验表明，AtlasVid显著提升了超高清长视频生成的效率，实现了高质量UHR长视频生成，速度提升60.9倍，训练成本显著降低，甚至优于原生4K视频生成器。
英文摘要：
Recent diffusion-based video generators have achieved remarkable visual fidelity and prompt controllability, yet scaling them to ultra-high-resolution (UHR) long videos remains prohibitively expensive. The difficulty is especially pronounced for long single-shot generation where a continuous scene must preserve global temporal coherence, and fine-grained spatial details without relying on clip transitions or autoregressive shot stitching. In this work, we revisit this challenge from the perspective of decoupled modeling. We argue that existing video diffusion models already encode strong local visual priors, while the main bottleneck lies in efficiently extending global spatiotemporal modeling as resolution and duration increase. Based on this insight, we propose AtlaVid, a decoupled global-local framework for efficient UHR long video generation. AtlaVid first generates a low-resolution and low-FPS global semantic proxy via temporally scaled RoPE, thereby extending the temporal horizon without increasing the training token count. Guided by this proxy, a high-resolution detail branch performs joint denoising with hierarchical locality-preserving attention. Reordered spatiotemporal windows preserve geometric locality and asymmetric global-local attention injects aligned semantic guidance and preserves the model's pretrained ability. This design enables resolution-agnostic training: the model is trained only at 720P with lightweight LoRA adaptation, yet generalizes directly to 4K and beyond for longer (>10s) video synthesis. Experiments show that AtlaVid substantially improves the efficiency of ultra-high-resolution long video generation, achieving high-quality UHR long video generation with 60.9x speed up and significantly less training cost and even better performance than native 4K video generators.
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