by Martina Haefeli | Jul 12, 2019 | Rendering, Visual Computing
Neural Importance Sampling We propose to use deep neural networks for generating samples in Monte Carlo integration. July 12, 2019ACM Siggraph 2019 Authors Thomas Müller (Disney Research/ETH Joint PhD) Brian McWilliams (Disney Research) Fabrice Rousselle...
by Martina Haefeli | Jun 16, 2019 | Machine Learning, Video Processing, Visual Computing
Neural Sequential Phrase Grounding (SeqGROUND) We propose an end-to-end approach for phrase grounding in images. June 16, 2019IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 Authors Pelin Dogan (Disney Research/ETH Joint PhD) Leonid...
by Martina Haefeli | Jun 10, 2019 | Machine Learning
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation We propose a novel,polynomial-time approximation of Shapley values in deep neural networks. June 10, 2019International Conference on Machine Learning (ICML) 2019 ...
by Martina Haefeli | Jun 1, 2019 | Animation, Story Technology, Visual Computing
Generating Animations from Screenplays In this paper, we develop a text-to-animation system which is capable of handling complex sentences. June 1, 2019*SEM 2019 Authors Yeyao Zhang (Disney Research/ETH Joint M.Sc.) Eleftheria Tsipidi (Disney Research) Sasha...
by Martina Haefeli | May 28, 2019 | Machine Learning, Video Processing, Visual Computing
Learning-based Sampling for Natural Image Matting We present a new sampling-based natural matting tech- nique that utilizes a pair of novel sampling networks for estimating background and foreground colors of pixels in unknown image regions. June 16, 2019IEEE...
