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Self-Supervised Effective Resolution Estimation with Adversarial Augmentations

Self-Supervised Effective Resolution Estimation with Adversarial Augmentations

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...
Production-Ready Face Re-Aging for Visual Effects

Production-Ready Face Re-Aging for Visual Effects

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...
Efficient Neural Style Transfer For Volumetric Simulations

Efficient Neural Style Transfer For Volumetric Simulations

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....
Deep Adaptive Sampling and Reconstruction using Analytic Distributions

Deep Adaptive Sampling and Reconstruction using Analytic Distributions

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...
Contrastive Learning for Controllable Blind Video Restoration

Contrastive Learning for Controllable Blind Video Restoration

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...
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