Bernhard Egger

TL;DR: A detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed is provided in this paper, where the challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and the state-of-the-art in each of these areas are reviewed.

TL;DR: In this article, the authors present an open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis, which considers symmetry, multi-scale and spatially varying details.

TL;DR: A detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed is provided, identifying unsolved challenges, proposing directions for future research, and highlighting the broad range of current and future applications.

TL;DR: This study demonstrates the large potential of synthetic data for analyzing and reducing the negative effects of dataset bias on deep face recognition systems and shows that current neural network architectures cannot disentangle face pose and facial identity, which limits their generalization ability.

TL;DR: This work proposes a fully automated, probabilistic and occlusion-aware 3D morphable face model adaptation framework following an analysis-by-synthesis setup and proposes a RANSAC-based robust illumination estimation technique.