by Martina Boldini | Jun 19, 2021 | Capture, Digital Humans, Machine Learning
Adaptive Convolutions for Structure-Aware Style Transfer We propose Adaptive convolutions; a generic extension of AdaIN, which allows for the simultaneous transfer of both statistical and structural styles in real time. June 19, 2021IEEE Conference on Computer...
by Martina Boldini | Nov 25, 2020 | Capture, Digital Humans, Machine Learning
Semantic Deep Face Models We present a method for nonlinear 3D face modeling using neural architectures. November 25, 20203D International Conference on 3D Vision (3DV) (2020) Authors Prashanth Chandran (DisneyResearch|Studios/ETH Joint PhD) Derek Bradley...
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 | Mar 24, 2020 | Animation, AR/VR, Visual Computing
Rig-space Neural Rendering Our idea is to render the character in many different poses and views, and to train a deep neural network to render high resolution images, from the rig parameters directly. Marc 24, 2020arxiv.org Authors Dominik Borer...
