by Melanie Cadalbert | Jun 4, 2023 | Capture
Kernel Aware Resampler In this paper we propose a framework for generic image resampling that not only addresses all the mentioned issues in the paper but extends the sets of possible transforms from upscaling to generictransforms. June 4, 2023 IEEE Conference on...
by Melanie Cadalbert | May 7, 2023 | Rendering, Visual Computing
Physics-Informed Neural Corrector for Deformation-based FluidControl We present a method to rectify deformed fluid flows using neural networks. Our neural corrector ensures the physical plausibilityof edited simulation footprints at test time, enabling interactive...
by Sarah Frigg | Jan 3, 2023 | Capture, Machine Learning, VFX
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, Machine Learning, VFX
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....
