  We address the challenging problem of jointly inferring the 3D flow and
volumetric densities moving in a fluid from a monocular input video with a deep
neural network. Despite the complexity of this task, we show that it is
possible to train the corresponding networks without requiring any 3D ground
truth for training. In the absence of ground truth data we can train our model
with observations from real-world capture setups instead of relying on
synthetic reconstructions. We make this unsupervised training approach possible
by first generating an initial prototype volume which is then moved and
transported over time without the need for volumetric supervision. Our approach
relies purely on image-based losses, an adversarial discriminator network, and
regularization. Our method can estimate long-term sequences in a stable manner,
while achieving closely matching targets for inputs such as rising smoke
plumes.
