no code implementations • 6 Nov 2023 • Vivek Jayaram, Ira Kemelmacher-Shlizerman, Steven M. Seitz
Our approach offers an efficient and accessible method for deriving personalized HRTFs and has the potential to greatly improve spatial audio experiences.
1 code implementation • 17 May 2021 • Vivek Jayaram, John Thickstun
This paper introduces an alternative approach to sampling from autoregressive models.
1 code implementation • NeurIPS 2020 • Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman
Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers.
1 code implementation • CVPR 2020 • Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman
To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites.
Ranked #1 on Image Matting on Adobe Matting
no code implementations • 31 Mar 2020 • Leonardo Citraro, Pablo Márquez-Neila, Stefano Savarè, Vivek Jayaram, Charles Dubout, Félix Renaut, Andrés Hasfura, Horesh Ben Shitrit, Pascal Fua
Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.
1 code implementation • ICML 2020 • Vivek Jayaram, John Thickstun
This paper introduces a Bayesian approach to source separation that uses generative models as priors over the components of a mixture of sources, and noise-annealed Langevin dynamics to sample from the posterior distribution of sources given a mixture.