  Artistically controlling fluid simulations requires a large amount of manual
work by an artist. The recently presented transportbased neural style transfer
approach simplifies workflows as it transfers the style of arbitrary input
images onto 3D smoke simulations. However, the method only modifies the shape
of the fluid but omits color information. In this work, we therefore extend the
previous approach to obtain a complete pipeline for transferring shape and
color information onto 2D and 3D smoke simulations with neural networks. Our
results demonstrate that our method successfully transfers colored style
features consistently in space and time to smoke data for different input
textures.
