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...
by Martina Megaro | Jul 30, 2021 | Rendering, Visual Computing
Deep Compositional Denoising for High-quality Monte Carlo Rendering We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernel-predicting denoisers can denoise more effectively. June 29, 2021Eurographics...
