Recent developments in 3D vision have enabled significant progress in inferring neural fluid fields and realistic rendering of fluid dynamics. However, these methods require dense captures of real-world flows, which demand specialized laboratory setups, making the process costly and challenging. Scientific machine learning (SciML) foundation models, pretrained on extensive simulations of partial differential equations (PDEs), encode rich multiphysics knowledge and thus provide promising sources of domain priors for fluid field inference. Nevertheless, the transferability of these foundation models to real-world vision problems remains largely underexplored. In this work, we demonstrate that SciML foundation models can significantly reduce the data requirements for inferring real-world 3D fluid dynamics while improving generalization. Our method leverages the strong forecasting capabilities and meaningful representations learned by SciML foundation models. We introduce a novel collaborative training strategy that equips neural fluid fields with augmented frames and fluid features extracted from the foundation model. Extensive experiments show substantial improvements in both quantitative metrics and visual quality over prior approaches. In particular, our method achieves a 9-36% improvement in peak signal-to-noise ratio (PSNR) for future prediction while reducing the number of required training frames by 25-50%. These results highlight the practical applicability of SciML foundation models for real-world fluid dynamics reconstruction. Our code is available at: https://github.com/delta-lab-ai/SciML-HY.

翻译：三维视觉领域的最新进展显著推动了神经流体场推断与流体动力学真实感渲染的发展。然而，现有方法通常需要对真实世界流体进行密集采集，这依赖于专业实验室配置，导致过程成本高昂且实施困难。科学机器学习（SciML）基础模型通过在大规模偏微分方程（PDEs）仿真数据上进行预训练，编码了丰富的多物理场知识，从而为流体场推断提供了极具潜力的领域先验来源。尽管如此，这些基础模型向真实世界视觉问题的可迁移性仍存在较大探索空间。本研究证明，SciML基础模型能够显著降低真实世界三维流体动力学推断的数据需求，同时提升泛化性能。我们的方法充分利用了SciML基础模型所具备的强预测能力及其学习到的语义化表征。我们提出了一种新颖的协同训练策略，通过从基础模型中提取增强帧序列与流体特征来赋能神经流体场。大量实验表明，相较于现有方法，本方法在定量指标与视觉质量上均取得显著提升。特别地，在减少25-50%训练帧数的条件下，本方法在未来预测任务中的峰值信噪比（PSNR）提升了9-36%。这些结果凸显了SciML基础模型在真实世界流体动力学重建中的实际应用价值。代码已开源：https://github.com/delta-lab-ai/SciML-HY。