| Date: | Friday, Sep. 2 |
|---|---|
| Time: | 14:30 |
| Location: | N10_302, Institute of Computer Science |
Our guest speaker is Katja Schwarz from the University of Tuebingen.
You are all cordially invited to the CVG Seminar on September 2nd at 2:30 p.m. CEST
What can we learn from 2D images? While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Recently, 3D-aware GANs have enabled explicit control over the camera pose and the generated content while training on 2D images, only. However, state-of-the-art 3D-aware generative models rely on coordinate-based MLPs which need to be queried for each sample along a camera ray, making volume rendering slow. Motivated by recent results in voxel-based novel view synthesis, I will introduce a sparse voxel grid representation for fast and 3D-consistent generative modeling in the first part of the talk.
In the second part, we will dive deeper into 2D GANs and investigate which spectral properties are learned from 2D images. Surprisingly, multiple recent works report an elevated amount of high frequencies in the spectral statistics which makes it straightforward to distinguish real and generated images. Explanations for this phenomenon are controversial: While most works attribute the artifacts to the generator, other works point to the discriminator. I will present our study on the frequency bias of generative models that takes a sober look at those explanations and provides insights on what makes proposed measures against high-frequency artifacts effective.
Katja is a 4th-year PhD student in the Autonomous Vision Group at Tuebingen University and is currently doing an internship with Sanja Fidler at NVIDIA. Katja received her BSc degree in 2016 and MSc degree in 2018 from Heidelberg University. In July 2019 she started her PhD at Tuebingen University under the supervision of Andreas Geiger. Her research lies at the intersection of computer vision and graphics and focuses on generative modeling in 2D and 3D.