 Dear Fellow Scholars, this is two-minute papers with Dr. Karajon Aifahir, approximately three years ago, a magical learning-based algorithm appeared that was capable of translating a photorealistic image of a zebra into a horse or the other way around could transform apples into oranges and more. Later, it became possible to do this even without the presence of a photorealistic image where all we needed was a segmentation map. This segmentation map provides labels on what should go where, for instance, this should be the road, here will be trees, traffic signs, other vehicles and so on. And the output was a hopefully photorealistic video and you can see here that the results were absolutely jaw-dropping. However, look, as time goes by, the backside of the car morphs and warps over time creating unrealistic results that are inconsistent even on the short term. In other words, things change around from second to second and the AI does not appear to remember what it did just a moment ago. This kind of consistency was solved surprisingly well in a follow-up paper from a video in which an AI would look at the footage of a video game, for instance, Pac-Man for approximately 120 hours, we could shut down the video game and the AI would understand the rules so well that it could recreate the game that we could even play with. It had memory and used it well and therefore it could enforce a notion of world consistency or, in other words, if we return to a state of the game that we visited before, it will remember to present us with very similar information. So the question naturally arises would it be possible to create a photorealistic video from the segmentation maps that is also consistent? And in today's paper, researchers at MVIDIA propose a new technique that requests some additional information, for instance, a depth map that provides a little more information on how far different parts of the image are from the camera. Much like the Pac-Man paper, this also has memory and I wonder if it is able to use it as well as that one did. Let's test it out. This previous work is currently looking at a man with a red shirt, with slowly look away, this regard the warping and when we go back, hey, do you see what I see here? The shirt became white. This is not because the person is one of those artists who can change their clothes in less than a second, but because this older technique did not have a consistent internal model of the world. Now, let's see the new one. Once again, we start with the red shirt, look away, and then, yes, same red to blue gradient. Excellent. So it appears that this new technique also reuses information from previous frames efficiently. It is finally able to create a consistent video with much less morphing and warping and even better. We have these advantages, consistency property, where if you look at something that we looked at before, we will see very similar information there. But there is more. Additionally, it can also generate scenes from new viewpoints, which we also refer to as neural rendering. And as you see, the two viewpoints show similar objects so the consistency property holds here too. And now, hold onto your papers because we do not necessarily have to produce these semantic maps ourselves. We can let the machines do all the work by firing up a video game that we like, request that the different object classes are colored differently and get this input for free. And then, the technique generated a photorealistic video from the game graphics. Absolutely amazing. Now note that it is not perfect. For instance, it has a different notion of time as the clouds are changing in the background rapidly. And look, at the end of the sequence, we get back to our starting point and the first frame that we saw is very similar to the last one. The consistency works here too. Very good. I have no doubt that two more papers down the line and this will be even better. And for now, we can create consistent photorealistic videos even if all we have is freely obtained video game data. What a time to be alive. If you are a researcher or a startup looking for cheap GPU compute to run these algorithms, check out Lambda GPU Cloud. I've talked about Lambda's GPU workstations in other videos and I'm happy to tell you that they are offering GPU cloud services as well. The Lambda GPU Cloud can train ImageNet to 93% accuracy for less than $19. Lambda's web-based IDE lets you easily access your instance right in your browser. And finally, hold onto your papers because the Lambda GPU Cloud costs less than half of AWS and Azure. Make sure to go to lambdalabs.com slash papers and sign up for one of their amazing GPU instances today. Our thanks to Lambda for helping us make better videos for you. Thanks for watching and for your generous support and I'll see you next time.