by Sarah Frigg | Sep 13, 2022 | Capture, Digital Humans, Machine Learning
Facial Animation with Disentangled Identity and Motion using Transformers We propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non-rigid motion during a performance. September 13, 2022ACM/Eurographics Symposium...
by Sarah Frigg | Jul 25, 2022 | Capture, Digital Humans, Machine Learning
Training a Deep Remastering Model We present a deep learning solution to bring the NTSC version to the new scan quality levels, which would be otherwise impossible with existing tools. July 24, 2022ACM SIGGRAPH 2022 Authors Abdelaziz Djelouah...
by Sarah Frigg | Jul 24, 2022 | Capture, Digital Humans, Machine Learning
Local Anatomically – Constrained Facial Performance Retargeting We present a new method for high-fidelity offline facial performance retargeting that is neither expensive nor artifact-prone. July 24, 2022ACM SIGGRAPH 2022 Authors Prashanth Chandran...
by Sarah Frigg | Jul 24, 2022 | Capture, Digital Humans, Machine Learning
MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling We demonstrate how MoRF is a strong new step towards 3D morphable neural head modeling. July 24, 2022ACM SIGGRAPH 2022 Authors Daoye Wang (ETH Zürich) Prashanth Chandran...
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...
