

Hyfluid
Overview :
HyFluid is a neural method for inferring fluid density and velocity fields from sparse, multi-view videos. Unlike existing neuro-fluid dynamics reconstruction methods, HyFluid accurately estimates density and reveals the underlying velocity, overcoming the inherent visual blurriness of fluid velocity. The method achieves physically plausible velocity field inference by introducing a set of physics-based losses, while handling the turbulent nature of fluid velocity. It employs a hybrid neural velocity representation, including a base neural velocity field that captures most of the irrotational energy and vortex particle velocities that simulate the remaining turbulent velocity. The method can be used for various learning and reconstruction applications surrounding 3D incompressible flows, including fluid resimulation and editing, future prediction, and neural dynamic scene synthesis.
Target Users :
HyFluid can be used to infer fluid density and velocity fields from videos, enabling fluid resimulation, future predictions, and dynamic neural scene synthesis.
Use Cases
Website: Rebuilding fluid fields from multi-view videos
Mini Program: Predicting future fluid dynamic evolution
Desktop Client: Dynamic neural scene synthesis
Features
Infer 3D fluid density and velocity fields from sparse, multi-view videos
Visualize the recovered 3D fluid fields
Resimulate new viewpoints
Future prediction
Dynamic neural scene synthesis
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