by Martina Megaro | Nov 30, 2021 | Capture, Machine Learning, VFX
Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering We propose to combine incomplete, high-quality renderings showing only facial skin with recent methods for neural rendering of faces, in order to automatically and...
by Martina Megaro | Jun 19, 2021 | Capture, Machine Learning, VFX
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 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 | 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,...
