by Martina Megaro | Jul 24, 2022 | Capture, Digital Humans, Machine Learning
Facial Hair Tracking for High Fidelity Performance Capture We demonstrate the proposed capture pipeline on a variety of different facial hair styles and lengths, ranging from sparse and short to dense full-beards. July 24, 2022ACM SIGGRAPH 2022 Authors...
by Martina Megaro | Apr 25, 2022 | Capture, Digital Humans, Machine Learning
Shape Transformers: Topology-Independent 3D Shape Models Using Transformers We present a new nonlinear parametric 3D shape model based on transformer architectures. April 25, 2022Eurographics 2022 Authors Prashanth Chandran (DisneyResearch|Studios/ETH Joint...
by Martina Megaro | Apr 25, 2022 | Capture, Digital Humans, Machine Learning
Improved Lighting Models for Facial Appearance Capture We compare the results obtained with a state-of-the-art appearance capture method [RGB∗20], with and without our proposed improvements to the lighting model. April 25, 2022Eurographics 2022 Authors...
by Martina Megaro | Nov 30, 2021 | Video Processing, Visual Computing
Neural Frame Interpolation for Rendered Content We propose solutions for leveraging auxiliary features to obtain better motion estimates, more accurate occlusion handling, and to correctly reconstruct non-linear motion between keyframes. November 30, 2021ACM...
by Martina Megaro | Nov 30, 2021 | Capture, Digital Humans, Machine Learning
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...
