 Dear Fellow Scholars, this is two-minute papers with Karoizhou and Aifahir. We've seen a lot of fluid and smoke simulations throughout the series. In each of these cases, the objective was to maximize the realism of these animations often to the point where they are indistinguishable from reality. However, there are cases where creating photorealistic footage is not the main objective. Artists often seek to imbue these fluid and smoke simulations with their own distinctive style and this style needs not to be photorealistic. It can be cartoonish, black and white, or take a variety of different color schemes. But unfortunately, to obtain such an effect, we have to sit down, get a bunch of papers, and draw the entirety of the animation frame by frame. And of course, to accomplish this, we also need to be physicists and know the underlying laws of fluid dynamics. That's not only borderline impossible, but extremely laborious as well. It would be really cool to have an algorithm that is somehow able to learn our art style and apply it to a fluid or smoke simulation sequence. But the question is, how do we exactly specify this style? Have a look at this really cool technique. I love the idea behind it. First, we compute a classical smoke simulation, then we freeze a few frames and get the artist to colorize them. After that, the algorithm tries to propagate this artistic style to the entirety of the sequence. Intuitively, this is artistic style transfer for fluid animations, but without using any machine learning techniques. Here, we are doing patch-based regenerative morphing. This awesome term refers to a technique that is trying to understand the direction of flows and that vac the colored regions according to it in a way that is both visually and temporarily coherent. Visually coherent means that it looks as close to plausible as we can make it, and temporarily coherent means that we are not looking only at one frame, but a sequence of frames, and the movement through these neighboring frames has to be smooth and consistent. These animation sequences were created from 8 to 9 colorized frames, and whatever you see happening in between was filled in by the algorithm. And again, we are talking about the artistic style here, not the simulation itself. A fine, handcrafted work in the world dominated by advanced learning algorithms. This paper is a bit like a beautiful handmade automatic timepiece in the era of quartz watches. If you enjoyed this episode, make sure to leave a like on the video, and don't forget to subscribe to get a glimpse of the future on the channel twice a week. Thanks for watching and for your generous support, and I'll see you next time.