by Sarah Frigg | Jan 3, 2023 | Capture, Digital Humans, Machine Learning
Self-Supervised Effective Resolution Estimation with Adversarial Augmentations We demonstrate that our method outperforms state-of-the-art image quality assessment methods in estimating the sharpness of real and generated human faces. January 3, 2023 IEEE Winter...
by Sarah Frigg | Nov 30, 2022 | Capture, Digital Humans, Machine Learning
Production-Ready Face Re-Aging for Visual Effects We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable...
by Sarah Frigg | Nov 30, 2022 | Rendering, Video Processing, Visual Computing
Efficient Neural Style Transfer For Volumetric Simulations We propose a simple feed-forward neural network architecture that is able to infer view-independent stylizations that are three orders of the magnitude faster than its optimization-based counterpart....
by Sarah Frigg | Nov 30, 2022 | Rendering, Video Processing, Visual Computing
Deep Adaptive Sampling and Reconstruction using Analytic Distributions We propose an adaptive sampling and reconstruction method for offline Monte Carlo rendering. November 30, 2022ACM SIGGRAPH Asia (2022) Authors Farnood Salehi (DisneyResearch|Studios) Marco...
by Sarah Frigg | Nov 21, 2022 | Rendering, Video Processing, Visual Computing
Contrastive Learning for Controllable Blind Video Restoration We demonstrate state of the art results compared to most recent video super-resolution and denoising methods. November 21, 2022British Machine Vision Conference (BMVC) (2022) Authors Givi Meishvili...
