While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that waste model capacity on low-level visual details - such as smoke, specular reflections, and fluid motion - rather than semantic structures essential for surgical understanding. We present UniSurg, a video-native foundation model that shifts the learning paradigm from pixel-level reconstruction to latent motion prediction. Built on the Video Joint Embedding Predictive Architecture (V-JEPA), UniSurg introduces three key technical innovations tailored to surgical videos: 1) motion-guided latent prediction to prioritize semantically meaningful regions, 2) spatiotemporal affinity self-distillation to enforce relational consistency, and 3) feature diversity regularization to prevent representation collapse in texture-sparse surgical scenes. To enable large-scale pretraining, we curate UniSurg-15M, the largest surgical video dataset to date, comprising 3,658 hours of video from 50 sources across 13 anatomical regions. Extensive experiments across 17 benchmarks demonstrate that UniSurg significantly outperforms state-of-the-art methods on surgical workflow recognition (+14.6% F1 on EgoSurgery, +10.3% on PitVis), action triplet recognition (39.54% mAP-IVT on CholecT50), skill assessment, polyp segmentation, and depth estimation. These results establish UniSurg as a new standard for universal, motion-oriented surgical video understanding.

翻译：尽管基础模型已推动了手术视频分析的发展，但当前方法主要依赖于像素级重建目标，这导致模型能力浪费在低层次视觉细节（如烟雾、镜面反射和流体运动）上，而非对手术理解至关重要的语义结构上。我们提出了UniSurg，一种视频原生基础模型，它将学习范式从像素级重建转变为潜在运动预测。基于视频联合嵌入预测架构（V-JEPA）构建，UniSurg针对手术视频引入了三项关键技术创新：1）运动引导的潜在预测，以优先处理具有语义意义的区域；2）时空亲和性自蒸馏，以强制关系一致性；3）特征多样性正则化，以防止在纹理稀疏的手术场景中出现表示坍缩。为实现大规模预训练，我们构建了迄今为止最大的手术视频数据集UniSurg-15M，包含来自13个解剖区域50个来源的3,658小时视频。在17个基准测试上的广泛实验表明，UniSurg在外科工作流识别（EgoSurgery上F1分数提升+14.6%，PitVis上提升+10.3%）、动作三元组识别（CholecT50上mAP-IVT为39.54%）、技能评估、息肉分割和深度估计等任务上均显著优于现有最先进方法。这些结果确立了UniSurg作为通用、面向运动的手术视频理解的新标准。