Baieri, DanieleCrisostomi, DonatoEsposito, StefanoMaggioli, FilippoRodolà, EmanueleCaputo, ArielGarro, ValeriaGiachetti, AndreaCastellani, UmbertoDulecha, Tinsae Gebrechristos2024-11-112024-11-112024978-3-03868-265-32617-4855https://doi.org/10.2312/stag.20241332https://diglib.eg.org/handle/10.2312/stag20241332In this work, we introduce an efficient and intuitive framework to produce synthetic multi-modal datasets of fluid simulations. The proposed pipeline 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 aim to exploit these properties to fulfill the community's need for standardized training data, fostering more reproducible and robust research. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios meaningfully. We demonstrate the properties of our contributions by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Physical simulation; Computer graphicsComputing methodologies → Physical simulationComputer graphics10.2312/stag.2024133210 pages