论文标题
液体的拉格朗日神经风格转移
Lagrangian Neural Style Transfer for Fluids
论文作者
论文摘要
在艺术中控制流体模拟的形状，运动和外观，在视觉效果产生中构成了主要挑战。在本文中，我们提出了一种神经样式转移方法，从图像到3D流体，以拉格朗日的观点提出。与基于网格的技术相比，使用粒子进行样式转移具有独特的好处。属性存储在颗粒上，因此通过粒子运动琐碎地运输。这本质上确保了优化的风格化结构的时间一致性，并显着提高了所得质量。同时，不必要的网格方法的昂贵，递归对齐速度场是不必要的，将计算时间减少到一个小时不到一个小时，并且在生产环境中实用了神经流动风格化。此外，拉格朗日表示可以改善艺术控制，因为它允许从图像进行多流体的风格化和一致的色彩传递，而该方法的一般性也能够对烟雾和液体的风格化。
Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.
