Our work explores temporal self-supervision for GAN-based video generation tasks. While
adversarial training successfully yields generative models for a variety of areas, temporal …

Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …

We study recovering fluid density and velocity from sparse multiview videos. Existing neural
dynamic reconstruction methods predominantly rely on optical flows; therefore, they cannot …

In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-
world smoke plumes. In addition, we propose a framework for accurate physics-based …

Artistically controlling fluids has always been a challenging task. Optimization techniques
rely on approximating simulation states towards target velocity or density field …

Abstract Existing Particle Imaging Velocimetry techniques require the use of high-speed
cameras to reconstruct time-resolved fluid flows. These cameras provide high-resolution …

We propose a novel method to reconstruct volumetric flows from sparse views via a global
transport formulation. Instead of obtaining the space-time function of the observations, we …

We propose a novel framework for real-time acquisition and reconstruction of temporally-
varying 3D phenomena with high quality. The core of our framework is a deep neural …

Visible light tomography is a promising and increasingly popular technique for fluid imaging.
However, the use of a sparse number of viewpoints in the capturing setups makes the …