Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.

本文提出了一种基于传输的神经风格迁移算法，可用于体状烟雾数据。采用优化技术近似仿真状态，从而使得自然图像的特征可以转移到烟雾模拟图像中，实现了从简单图案到复杂结构的通用内容感知操纵，通过控制升降运算和无旋动能势可以实现传输引起的风格化速度场的直接控制，最终生成的 3D 重建图像可无缝地合并不同摄像机视角的风格化效果。

烟雾模拟的基于传输的神经风格迁移

Transport-Based Neural Style Transfer for Smoke Simulations

We present a novel method to interpolate smoke and liquid simulations in
order to perform data-driven fluid simulations. Our approach calculates a dense
space-time deformation using grid-based signed-distance functions of the
inputs. A key advantage of this implicit Eulerian representation is that it
allows us to use powerful techniques from the optical flow area. We employ a
five-dimensional optical flow solve. In combination with a projection
algorithm, and residual iterations, we achieve a robust matching of the inputs.
Once the match is computed, arbitrary in between variants can be created very
efficiently. To concatenate multiple long-range deformations, we propose a
novel alignment technique. Our approach has numerous advantages, including
automatic matches without user input, volumetric deformations that can be
applied to details around the surface, and the inherent handling of topology
changes. As a result, we can interpolate swirling smoke clouds, and splashing
liquid simulations. We can even match and interpolate phenomena with
fundamentally different physics: a drop of liquid, and a blob of heavy smoke.

介绍了一种将烟雾和液体模拟进行数据驱动流体模拟的新方法，该方法使用基于格网的有符号距离函数计算密集的时空变形，并结合光流求解、投影算法和残差迭代实现了输入匹配。此外还提出了对齐技术，它可以连接多个长距离形变，从而实现旋转烟云或飞溅液体等现象。