 Dear Fellow Scholars, this is two-minute papers with Karoizsol Naifahir. Today we are going to talk about a great algorithm that takes the facial expression of one human and transfers it onto someone else. First, there is a calibration step where the algorithm tries to capture the geometry and the reflectance properties of both faces. The expression transfer comes after this, which is fraught with difficulties. It has to be able to deal with changes in the geometry. The reflectance properties of the face, the illumination in the room, and finally changes in pose and expressions. All of this at the same time and with an negligible time delay. The difficulty of the problem is further magnified by the fact that we humans know really well how a human face is meant to move, therefore even the slightest inaccuracies are very easily caught by our eyes. Add this to the fact that one has to move details like additional wrinkles to a foreign face correctly, and it's easy to see that this is an incredibly challenging problem. And the resulting technique not only does the expression transfer quite well, but is also robust for lighting changes. However, it is not robust for occlusions, meaning that errors should be expected when something gets in the way. Problems also arise if the face is turned away from the camera, but the algorithm recovers from these erroneous states rapidly. What's even better, if you use this technique you can also cut back on your plastic surgery and hair plantation costs. How cool is that? This new technique promises tons of new possibilities. Beyond the obvious impersonation and reenactment fund for the motion picture industry, the authors propose the following in the paper. Imagine another setting in which you could reenact a professionally captured video of somebody in business attire with a new real-time face capture of yourself sitting in casual clothing on your sofa. Hell yeah! Thanks for watching and I'll see you next time.