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...
by Martina Megaro | Jul 24, 2022 | Capture, Machine Learning, VFX
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 Sarah Frigg | Jul 24, 2022 | Capture, Machine Learning, VFX
Implicit Neural Representation for Physics-driven Actuated Soft Bodies We apply the method to volumetric soft bodies, human poses, and facial expressions, demonstrating artist-friendly properties. July 24, 2022ACM SIGGRAPH 2022 Authors Lingchen Yang (ETH...
by Martina Megaro | Apr 25, 2022 | Capture, Machine Learning, VFX
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...
