by Martina Haefeli | Aug 17, 2020 | Capture, Digital Humans, Machine Learning
Data-driven Extraction and Composition of Secondary Dynamics in Facial Performance Capture Our work aims to compute and characterize the difference between the captured dynamic facial performance, and a speculative quasistatic variant of the same motion should the...
by Martina Haefeli | Aug 14, 2020 | Capture, Digital Humans, Machine Learning
Single-Shot High-Quality Facial Geometry and Skin Appearance Capture We propose a new light-weight face capture system capable of reconstructing both high-quality geometry and detailed appearance maps from a single exposure. August 14, 2020ACM Siggraph 2020 ...
by Martina Haefeli | Jul 3, 2020 | Capture, Digital Humans, Machine Learning
Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm We propose a novel interactive method for the creation of digital faces that is simple and intuitive to use, even for novice users, while consistently producing plausible 3D face geometry,...
by Martina Haefeli | Jun 29, 2020 | Capture, Machine Learning
High-Resolution Neural Face Swapping for Visual Effects We propose an algorithm for fully automatic neural face swapping in images and videos. June 29, 2020Eurographics Symposium on Rendering (2020) Authors Jacek Naruniec (DisneyResearch|Studios) Leonhard...
by Martina Haefeli | Jun 16, 2020 | Capture, Digital Humans, Machine Learning
Attention-Driven Cropping for Very High Resolution Facial Landmark Detection Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today. June 16, 2020IEEE Conference on...
