 Dear Fellow Scholars, this is Two Minute Papers with Dr. Karojona Ifehir. Today we are going to see how Nvidia's crazy new AI is able to understand basically any human movement. So, how is this even possible? And even more importantly, what is pose estimation? Well, simple, a video of people goes in and the posture they are taking comes out. Now, you see here that previous techniques can already do this quite well. What's more, if we allow an AI to read the Wi-Fi signals bouncing around in the room, it can perform pose estimation even through walls. Kind of. Now, you may be wondering what is pose estimation good for? By the end of this video, you will see that this can help us move around in virtual worlds the metaverse, if you will. But, let's not rush there yet because still, there are several key challenges here. One, we need to be super accurate to even put a dent into this problem. Why? Well, because if we have a video and we are off by just a tiny bit from frame to frame, this kind of flickering may happen. That's a challenge. Two, foot sliding. Yes, you heard it right, previous methods suffer from this phenomenon. You can see it in action here. And also here too. So, why does this happen? It happens because the technique has no knowledge of the physics of real human movements. So, scientists at Nvidia, the University of Toronto and the Vector Institute fired up a collaboration and when I first heard about their concept, I thought you are doing what? But, check this out. First, they perform a regular pose estimation. Of course, this is no good as it has the dreaded temporal inconsistency or in other words flickering. And in other cases, often, foot sliding too. Now, hold on to your papers because here comes the magic. They transfer the motion to a video game character and embed that character in a physics simulation. In this virtual world, the motion can be corrected to make sure they are physically correct. Now, remember, foot sliding happens because of the lack of knowledge in physics. So, perhaps this idea is not that crazy after all. Let's have a look. Now this will be quite a challenge, explosive sprinting motions. And... Whoa! This is amazing. This, dear fellow scholars, is superb pose estimation and tracking. And, how about this? A good tennis serve includes lots of dynamic motion. And, just look at how beautifully it reconstructs this move. Apparently, physics works. Now, the output needn't be stickman. We can retarget these to proper textured virtual characters built from a triangle mesh. And, that is just one step away from us being able to appear in the metaphors. No head mounted displays are required, no expensive studio, and no motion capture equipment is required. So, what is required? Actually, nothing. Just the raw input video of us. That is insanity. And, all this produces physically correct motion. So, that crazy idea about taking people and transforming them into video game characters is not so crazy after all. So, now we are one step closer to be able to work and even have some coffee together in a virtual world. What a time to be alive. This video has been supported by weights and biases. And, being a machine learning researcher means doing tons of experiments and, of course, creating tons of data. But, I am not looking for data, I am looking for insights. And, weights and biases helps with exactly that. They have tools for experiment tracking, data set and model versioning, and even hyper parameter optimization. No wonder this is the experiment tracking tool choice of open AI, Toyota Research, Samsung, and many more prestigious labs. Make sure to use the link wnb.me slash paper intro, or just click the link in the video description. And, try this 10 minute example of weights and biases today to experience the wonderful feeling of training a neural network and being in control of your experiments. After you try it, you won't want to go back. Thanks for watching and for your generous support, and I'll see you next time.