A machine learning system learns your design preferences from examples, then generates and scores furniture and building shapes to match your taste. It shows confidence in its predictions with orange bars for scores and blue bars for uncertainty, letting you find tables with one leg or crossed designs that fit your style.
Dear fellow scholars, this is two minute papers with Creating geometry for a computer game or a movie is a very long and arduous task. For instance, if we would like to populate a virtual city with buildings, it would cost a ton of time and money and of course we would need quite a few artists. This piece of work solves this problem in a very elegant and convenient way. It learns the preference of the user, then creates and recommends a set of solutions that are expected to be desirable. In this example, we are looking for tables with either one leg or crossing legs. It should also be properly balanced. Therefore, if we see any of these criteria, we'll assign a high score to these models. These are the preferences that the algorithm should try to learn. The orange bars show the predicted score for new models created by the algorithm. A larger value means that the system expects the user to score these high, and the blue bars mean uncertainty. Generally, we are looking for solutions with large, orange and small blue bars. This means that the algorithm is confident that a given model is in line with our preferences and we get exactly what we were looking novel balanced table designs with one leg or crossed legs. Interestingly, since we have these uncertainty values, one can also visualize counter examples where the algorithm is not so sure, but would guess that we wouldn't like the model. It's super cool that it is aware how horrendous these designs look. It may have a better eye than many of the contemporary art curators out there. There are also examples where the algorithm is very confident that we are going to hate a given example because of its legs or unbel balancedness and would never recommend such a model. So indirectly, it also learns how a balanced piece of furniture should look like without ever learning the concept of gravity or doing any kind of architectural computation. The algorithm also works on buildings, and after learning our preferences, it can populate entire cities with geometry that is in line with our artistic vision. Excellent piece of work. Thanks for watching and for your generous support and I'll see you next time.
