Abstract

Abstract: Recently, generative models for 3D objects are gaining much popularity in virtual (VR) and augmented reality (AR) applications. Training such models using standard 3D representations, like voxels or point clouds, is challenging and requires complex tools for proper colour rendering. In order to overcome this limitation, Neural Radiance Fields (NeRFs) offer a state-of-the-art quality in synthesizing novel views of complex 3D scenes from a small subset of 2D images.

In the presentation, I describe generative models which use hypernetworks paradigm to produce 3D objects represented by NeRF. The advantage of the models over existing approaches is that it produces a dedicated NeRF representation for the object without sharing some global parameters of the rendering component.

Bio: Dr Hab. Przemyslaw Spurek received a master’s degree in mathematics and a PhD in computer science from the Jagiellonian University, Krakow, Poland, in 2009 and 2014, respectively. He is currently an Assistant Professor at the Institute of Computer Science, Jagiellonian University. He co-authored several research papers published in well-known journals and presented at the top Machine Learning conferences, including NeurIPS, ICML , AISTATS, and IROS . His research interests include deep learning, especially generative models, and meta-learning.