 Dear Fellow Scholars, this is 2 Minute Papers with Károly Zsolnai-Fehér. If we have an animation movie or a computer game with quadrupeds and we are yearning for really high quality, life-like animations, motion capture is often the go-to tool for the job. Motion capture means that we put an actor, in our case a dog in the studio, we ask it to perform sitting, trotting, pacing and jumping, record this motion and transfer it onto our virtual character. In an earlier work, a learning-based technique was introduced by the name Mode Adaptive Neural Network and it was able to correctly weave together these previously recorded motions and not only that, but it also addressed these unnatural sliding motions that were produced by previous works. As you see here, it also worked well on more challenging landscapes. We talked about this paper approximately 100 videos or, in other words, a little more than a year ago and I noted that it was scientifically interesting, it was evaluated well, it had all the ingredients for a truly excellent paper. But one thing was missing. So, what is that one thing? Well, we haven't seen the characters interacting with the scene itself. If you like this previous paper, you are going to be elated by this new one because this new work is from the very same group and goes by the name Neural State Machine and introduces character-seen interactions for bipeds. Now, we suddenly jumped from a quadruped paper to a biped one and the reason for this is that because I was looking to introduce the concept of food-sliding, which will be measured later for this new method too. Stay tuned. So, in this new problem formulation, we need to guide the character to a challenging and state, for instance, sitting in a chair while being able to maneuver through all kinds of geometry. We'll use the chair example a fair bit in the next minute or two. So, I'll stress that this can do a whole lot more, the chair is just used as a vehicle to get a taste of how this technique works. But the end state needn't just be some kind of chair. It can be any chair. This chair may have all kinds of different heights and shapes and the agent has to be able to change the animations and stitch them together correctly regardless of the geometry. To achieve this, the authors propose an interesting new data augmentation model. Since we are working with Neural Networks, we already have a training set to teach it about motion and data augmentation means that we extend this data set with lots and lots of new information to make the AI generalize better to unseen real world examples. So, how is this done here exactly? Well, the authors proposed a clever idea to do this. Let's walk through their five prescribed steps. One, let's use motion capture data, have the subject sit down and see what the contact points are when it happens. Two, we then record the curves that describe the entirety of the motion of sitting down. So far so good, but we are not interested in one kind of chair. We wanted to sit into all kinds of chairs, so three, generate a large selection of different geometries and adjust the location of these contact points accordingly. Four, change the motion curves so they indeed end at the new transformed contact points. And five, move the joints of the character to make it follow this motion curve and compute the evolution of the character pose. And then pair up this motion with the chair geometry and chuck it into the new augmented training set. Now make no mistake, the paper contains much, much more than this, so make sure to have a look in the video description. So what do we get for all this work? Well, have a look at this trembling character from a previous paper and now look at the new synthesized motions. Natural, smooth, creamy and I don't see artifacts. Also, here you see some results that measure the amount of food sliding during these animations, which is subject to minimization. That means that the smaller the bars are, the better. With NSM, you see how this neural state machine method produces much less than previous methods. And now we see how cool it is that we talked about the quadruped paper as well because we see that it even beats the M A and N, the mode adaptive neural networks from the previous paper. That one had very little food sliding and apparently it can be improved by quite a bit. The positional and rotational errors in the animation it offers are also by far the lowest of the bunch. Since it works in real time, it can also be used for computer games and virtual reality applications. And all this improvement within one year of work. 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. Thanks for watching and for your generous support and I'll see you next time.