In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community's need for standardized training data, fostering more reproducibile and robust research.

翻译：本文提出了一种高效生成流体模拟合成多模态数据集的流程。该流程能够再现流体动力学行为，并支持从不同视角和模态探索与学习其复杂行为的多种特征。我们利用该框架生成一系列精心设计的训练数据集，这些数据集以有意义的方式覆盖特定流体模拟场景。通过使用我们的基准数据集评估近期发表的神经流体模拟和流体逆渲染任务算法，验证了本工作的特性。我们的贡献旨在满足学界对标准化训练数据的需求，从而促进更可复现、更稳健的研究。