 Dear Fellow Scholars, hold onto your papers because we have an emergency situation. Everybody can make deepfakes now by recording a voice sample such as this one and the lips of the target subject will move as if they themselves were saying this. Not bad huh? And now let's see what else this new method can do. First let's watch this short clip of a speech and make sure to pay attention to the fact that the louder voice is the English translator and if you pay attention you can hear the chancellor's original voice in the background too. Framework for our future cooperation. Day Manuel. Only 8 months ago you were awarded the... So what is the problem here? It's strictly speaking, there is no problem here, this is just the way the speech was recorded. However, what if we could recreate this video in a way that the chancellor's lips would be synced not to her own voice but to the voice of the English interpreter? This would give an impression as if the speech was given in English and the video content would follow what we hear. Now that sounds like something straight out of a science fiction movie, perhaps even with today's advanced machine learning techniques, but let's see if it's possible. This is a state of the art technique from last year that attempts to perform this. Um, development in a very different history. We signed this on the 56th anniversary of the illicit treaty of 1966. Hmm, there are extraneous lip movements which are the remnants of the original video so much so that she seems to be giving two speeches at the same time, not too convincing. So is this not possible to pull off? Well, now hold onto your papers and let's see how this new paper does at the same problem. On Germany but also for a starting point of a very different development in a very different history. We signed this on the 56th anniversary of the illicit treaty of 9th. Wow, now that's significantly better. The remnants of the previous speech are still there, but the footage is much, much more convincing. What's even better is that the previous technique was published just one year ago by the same research group. Such a great leap in just one year. My goodness. So apparently this is possible. But I would like to see another example just to make sure. That's what he'd like to on the first issue, it's not for me to comment on the visit of Madame. Checkmark. So far this is an amazing leap, but believe it or not, this is just one of the easier applications of the new model. So let's see what acid can do. For instance, many of us are sitting at home yearning for some learning materials, but the vast majority of these were recorded in only one language. Before if we could read up famous lectures into many other languages. The computer vision is moving on the basis of the deep learning very quickly. For example, we use the same color as the car now to make a lot of things. Look at that. Any lecture could be available in any language and look as if they were originally recorded in these foreign languages as long as someone says the words, which can also be kind of automated through speech synthesis these days. So, it clearly works on real characters, but are you thinking what I am thinking? Three, what about lip syncing animated characters? Imagine if a line has to be changed in a Disney movie, can we synthesize new video footage without calling in the animators for yet another all-nighter? Let's give it a try. I'll go around. Wait for my call. Indeed we can, loving it. Let's do one more. Four, of course we have a lot of these meme gifts on the internet. What about redubbing those with an arbitrary line of our choice? Yup, that is indeed also possible. Well done. And imagine that this is such a leap, just one more work down the line from the 2019 paper, I can only imagine what results we will see one more paper down the line. It not only does what it does better, but it can also be applied to a multitude of problems. What a time to be alive. When we look under the hood, we see that the two key components that enable this wizardry are here and here. So what does this mean exactly? It means that we jointly improve the quality of the lip syncing and the visual quality of the video. These two modules curate the results offered by the main generator neural network and reject solutions that don't have enough detail or don't match the speech that we hear and thereby they steer it towards much higher quality solutions. If we continue this training process for 29 hours for the lip syncing discriminator, we get these incredible results. Now let's have a quick look at the user study and humans appear to almost never prefer the older method compared to this one. I tend to agree. If you consider these forgeries to be defects, then there you go. Useful defects that can potentially help people around the world stranded at home to study and improve themselves. Imagine what good this could do. Well done. This episode has been supported by Lambda GPU Cloud. If you're looking for inexpensive Cloud GPUs for AI, check out Lambda GPU Cloud. They recently launched Quadro RTX 6000, RTX 8000 and V100 instances and hold onto your papers because Lambda GPU Cloud can cost less than half of AWS and Asia. Plus they are the only Cloud service with 48GB RTX 8000. Join researchers at organizations like Apple, MIT and Caltech in using Lambda Cloud instances workstations or servers. Make sure to go to lambdaleps.com slash papers to sign up for one of their amazing GPU instances today. Our thanks to Lambda for their longstanding support and for helping us make better videos for you. Thanks for watching and for your generous support and I'll see you next time.