9 DAILY WACV Saturday Fine-Tuning Image-Conditional … says. “In this case, it's monocular, which is a pretty ill-posed problem, making it very difficult. And in that sense, you do require a strong prior from a model. And thus, our work focuses on that in utilizing a very strong prior such as stable diffusion for our predictions.” People coming to Gonzalo’s poster and oral presentations will discover that you can use diffusion models for these geometry tasks in an efficient manner. You can do single to few steps inference. They will also find out that you can directly fine tune these models on a desired downstream task. In this case, it's depth and surface normal estimation. But for any geometric task that someone's interested in, they can now train it end-to-end and get very fast and reliable inference. There are not many master's students who are first authors on major conferences’ accepted papers, so congratulations to Gonzalo. What makes him the proudest? “I think having gone through the whole research process, reading all papers, gathering knowledge, trying everything out and finding kind of the light, finding an idea and keep pursuing it and then getting a breakthrough, writing the paper”, is Gonzalo’s extensive reply. “And the whole process was very, very interesting and very much fun!”
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