Cubify All The Things! 🐄

Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. I apologize for my voice today, I am trapped in this frail human body, and sometimes it falters. But, the papers must go on. This is one of those papers where I find that more time I spend with it, the more I realize how amazing it is. It starts out with an interesting little value proposition that in and of itself, would likely not make it to a paper. So, what is this paper about? Well, as you see here, this one is about cubification of 3D geometry. In other words, we take an input shape, and it stylizes it to look more like a cube. Okay, that’s cute, especially given that there are many-many ways to do this, and it’s hard to immediately put into words what a good end result would be, you can see a comparison to previous works here. These previous works did not seem to preserve a lot of fine details, but if you look at this new one, you see that this one does that really well. Very nice indeed. But still…when I read this paper, at this point, I was thinking…I’d like a little more. Well, I quickly found out that this work has more up its sleeve. So much more. Let’s talk about 7 of these amazing features. For instance, one, we can control the strength of the transformation with this lambda parameter, as you see, the more we increase it, the more heavy-handed the smushing process is going to get. Please remember this part. Two, we can also cubify selectively along different directions, or, select parts of the object that should be cubified differently. Hmm. Okay. Three, we can even use it to fix flaws in the input 3D geometry. Four, this transformation procedure also takes into consideration the orientations - this means that we can perform it from different angles, which gives us a large selection of possible outputs for the same model. Five, it is fast and works on high-resolution geometry, and you see different settings for the lambda parameter here that is the same parameter as we have talked about before - the strength of the transformation. Six, we can also combine many of these features interactively until a desirable shape is found. Seven is about to come in a moment, but to appreciate what that is, we have to look at …this. To perform what you have seen here so far, we have to minimize this expression. This first term says ARAP, as rigid as possible, which stipulates that whatever we do in terms of smushing, it should preserve the fine local features. The second part is called a regularization term that encourages sparser, more axis-aligned solutions so we don’t destroy the entire model during this process. The stronger this term is, the bigger say it has in the final results, which, in return, become more cube-like. So, how do we do that? Well, of course, with our trusty little lambda parameter. Not only that, but if we look at the appendix, it tells us that we can generalize this second regularization term for many different shapes. So here we are, finally, seven, it doesn’t even need to be cubification, we can specify all kinds of polyhedra. Look at those gorgeous results. I love this paper. It is playful, it is elegant, it has utility, and, it generalizes well. It doesn’t care in the slightest what the current mainstream ideas are and invites us into its own little world. In summary, this will serve all your cubification needs, and turns out, it might even fix your geometry, and more. I would love to see more papers like this. In this series, I try to make people feel how I feel when I read these papers. I hope I have managed this time, but you be the judge. Let me know

in the comments. Thanks for watching and for your generous support, and I'll see you next time!