 Dear Fellow Scholars, this is two minute papers with Karo Zsolnai-Fehir. With today's graphics technology, we can enjoy many really smooth videos that were created using 60 frames per second. We love it too, and we hope that you noticed that our last 100 or maybe even more episodes have been available in 60 hertz. However, it often times happens that we are given videos that have anything from 20 to 30 frames per second. This means that if we play them on a 60 FPS timeline, half or even more of these frames will not provide any new information. As we try to slow down the videos for some nice slow motion action, this ratio is even worse, creating an extremely choppy output video. Fortunately, there are techniques that are able to guess what happens in these intermediate frames and give them to us. This is what we call frame interpolation. We have had some previous experiments in this area where we tried to create an amazing slow motion version of a video with some bubbles merging. A simple and standard way of doing frame interpolation is called frame blending, which is a simple averaging of the closest two known frames. The more advanced techniques are optical flow-based, which is a method to determine what motions happened between these two frames and create new images based on that knowledge leading to higher quality results in most cases. This technique uses a convolutional neural network to accomplish something similar, but in the end, it doesn't give us an image but a set of convolution kernels. This is a transformation that is applied to the previous and the next frame to produce an intermediate image. It is not the image itself, but the recipe of how to produce it, if you will. We've had a ton of fun with convolutions earlier where we use them to create beautiful subsurface scattering effects for translucent materials in real time and are more loyal fellow-scalers remembered that at some point I also pulled out my guitar and showed what it would sound like inside a church using a convolution-based reverberation technique. The links are available in the video description, make sure to check them out. Since we have a neural network over here, it goes without saying that the training takes place on a large number of before, after image pairs so that the network is able to produce these convolution kernels. Of course, to validate this algorithm, we also need to have access to a ground truth reference to compare against. We can accomplish this by withholding some information about a few intermediate frames, so we have the true images which the algorithm would have to reproduce without seeing it. Kind of like giving a test to a student when we already know the answers. You can see such a comparison here. And now, let's have a look at these results. As you can see, they are extremely smooth and the technique retains a lot of high frequency details in these images. The videos also seem temporalic adherent, which means that it's devoid of the annoying flickering effect where the reconstruction takes place in a way that's a bit different in each subsequent frame. Kind of that happens here, which is an excellent property of this technique. The Python source code for this technique is available and is free for non-commercial uses. I've put a link in the description if you have given it a try and have some results of your own, make sure to post them in the comments section or our subreddit discussion. The link is available in the description. Thanks for watching and for your generous support, and I'll see you next time.