71 citations · 173 across the 9 of their papers we have counts for
11 papers
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformat…
Towards Attention-aware Foveated Rendering
Brooke Krajancich, Petr Kellnhofer, Gordon Wetzstein
Foveated graphics is a promising approach to solving the bandwidth challenges of immersive virtual and augmented reality displays by exploiting the falloff in spatial acuity in the…
Generative Neural Articulated Radiance Fields
Alexander W. Bergman, Petr Kellnhofer, Wang Yifan +3
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, h…
Fast Training of Neural Lumigraph Representations using Meta Learning
Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzstein
Novel view synthesis is a long-standing problem in machine learning and computer vision. Significant progress has recently been made in developing neural scene representations and…
A Perceptual Model for Eccentricity-dependent Spatio-temporal Flicker Fusion and its Applications to Foveated Graphics
Brooke Krajancich, Petr Kellnhofer, Gordon Wetzstein
Virtual and augmented reality (VR/AR) displays strive to provide a resolution, framerate and field of view that matches the perceptual capabilities of the human visual system, all…
Neural Lumigraph Rendering
Petr Kellnhofer, Lars Jebe, Andrew Jones +3
Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural rendering techniques have recently achieved photorealistic image quality for this task. State-…
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Eric R. Chan, Marco Monteiro, Petr Kellnhofer +2
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in…
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Petr Kellnhofer, Adria Recasens, Simon Stent +2
Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale gaze-tracking dataset and method for robust 3D gaze estimation…
Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura +3
Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a…
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
Adrià Recasens, Petr Kellnhofer, Simon Stent +2
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer…
Eye Tracking for Everyone
Kyle Krafka, Aditya Khosla, Petr Kellnhofer +4
From scientific research to commercial applications, eye tracking is an important tool across many domains. Despite its range of applications, eye tracking has yet to become a perv…