Talk Abstract: In this talk, we are going to review data structures that have recently been used in generative modeling, e.g. in generative adversarial networks, transformer-based models, and generative diffusion models. Many of these data structures have been proposed in the context of encoding shapes as neural fields, e.g. neural radiance fields, signed distance functions, or occupancy functions. We will also discuss recently proposed data structures based on irregular grids and a set of latent vectors from our own work accepted at NeurIPS 2022 and Siggraph 2023.

Speaker Biography: Peter Wonka is a Full Professor in Computer Science at King Abdullah University of Science and Technology (KAUST) and Interim Director of the Visual Computing Center (VCC). Peter Wonka received his doctorate from the Technical University of Vienna in computer science. Additionally, he received a Master of Science in Urban Planning from the same institution. After his Ph.D., Dr. Wonka worked as a postdoctoral researcher at the Georgia Institute of Technology and as faculty at Arizona State University. His research publications tackle various topics in computer vision, computer graphics, and machine learning. The current research focus is on deep learning, generative models, and 3D shape analysis and reconstruction..