by Sarah Frigg | Oct 5, 2022 | Capture, Machine Learning, VFX
Learning Dynamic 3D Geometry and Texture for Video Face Swapping We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with...
by Sarah Frigg | Sep 13, 2022 | Capture, Machine Learning, VFX
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, Machine Learning, VFX
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, Machine Learning, VFX
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, Machine Learning, VFX
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...
