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Semantic Deep Face Models

Semantic Deep Face Models

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...
Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm

Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm

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,...
High-Resolution Neural Face Swapping for Visual Effects

High-Resolution Neural Face Swapping for Visual Effects

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...
Rig-space Neural Rendering

Rig-space Neural Rendering

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...
Differentiable Surface Splatting for Point-based Geometry Processing

Differentiable Surface Splatting for Point-based Geometry Processing

by Martina Haefeli | Nov 1, 2019 | Machine Learning, Visual Computing

Differentiable Surface Splatting for Point-based Geometry Processing We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. November 1, 2019ACM SIGGRAPH Asia 2019   AuthorsYifan Wang (ETH Zurich)Serena Felice...
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