by Martina Boldini | Jul 30, 2021 | Rendering, Visual Computing
Deep Compositional Denoising for High-quality Monte Carlo Rendering We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernel-predicting denoisers can denoise more effectively. June 29, 2021Eurographics...
by Martina Boldini | Jun 19, 2021 | Capture, Digital Humans, Machine Learning
Adaptive Convolutions for Structure-Aware Style Transfer We propose Adaptive convolutions; a generic extension of AdaIN, which allows for the simultaneous transfer of both statistical and structural styles in real time. June 19, 2021IEEE Conference on Computer...
by Martina Boldini | May 8, 2021 | Video Processing, Visual Computing
Lossy Image Compression with Normalizing Flows We propose a deep image compression method that is similarly able to go from low bit-rates to near lossless quality, byleveraging normalizing flows to learn a bijective mapping from the image space toa latent...
by Martina Boldini | May 3, 2021 | Video Processing, Visual Computing
Robust Image Denoising using Kernel Predicting Networks We present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. May 3, 2021Eurographics 2021 Authors Zhilin Cai (DisneyResearch|Studios/ETH...
by Martina Boldini | May 3, 2021 | Video Processing, Visual Computing
Deep HDR estimation with generative detail reconstruction We present qualitative and quantitative comparisons with existing techniques where our method achieves state-of-the-art performance. May 3, 2021Eurographics 2021 Authors Yang Zhang...
