by Martina Megaro | Nov 25, 2020 | Capture, Machine Learning, VFX
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 Megaro | Nov 10, 2020 | Video Processing, Visual Computing
Deep Deinterlacing We propose a novel approach to deep video deinterlacing. November 10, 2020SMPTE Annual Technical Conference & Exhibition (2020) Authors Michael Bernasconi (DisneyResearch|Studios) Abdelaziz Djelouah (DisneyResearch|Studios) Sally...
by Martina Megaro | Aug 17, 2020 | Capture, Machine Learning, VFX
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 Megaro | Aug 14, 2020 | Capture, Machine Learning, VFX
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 Megaro | Jul 3, 2020 | Capture, Machine Learning, VFX
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,...
