ABOUT
Alexander Sorkine-Hornung
I am Research Scientist at Facebook Zurich.
We are hiring!
If you are interested in working on the future of AR/MR/VR/XR/*R, and you have outstanding skills in computer vision, graphics, machine learning, software engineering, design, prototyping, or any other relevant area, please contact me.
SHORT CV
See here for complete version
RESEARCH SCIENTIST @ FACEBOOK
06/2017 - present
Technical lead for Mixed Reality at Facebook Zurich, working on shipping the future of AR/MR/VR/XR/*R.
RESEARCH SCIENTIST/MANAGER @ DISNEY RESEARCH
11/2009 - 05/2017
Head of the Imaging and Video group at Disney Research Zurich, performing research at the interface of computer vision, graphics, and machine learning. The technologies developed by my group have shipped in various Disney park attractions and movie productions including Soarin’ over California and Soarin’ around the World, Pirates of the Caribbean, Maleficent, Cinderella, Big Hero 6, and others.
PHD IN COMPUTER SCIENCE @ RWTH-AACHEN
10/2008
Thesis: Shape Representations for Image-based Applications. Graduated Summa cum laude.
SELECTED PUBLICATIONS
See Google Scholar for a complete list
PASSTHROUGH+
REAL-TIME STEREOSCOPIC VIEW SYNTHESIS FOR MOBILE MIXED REALITY
Gaurav Chaurasia, Arthur Nieuwoudt, Alexandru-Eugen Ichim, Richard Szeliski, Alexander Sorkine-Hornung
AN INTEGRATED 6DOF VIDEO CAMERA AND SYSTEM DESIGN
[Paper]
Albert Parra Pozo, Michael Toksvig, Terry Filiba Schrager, Joyce Hsu, Uday Mathur, Alexander Sorkine-Hornung, Rick Szeliski, Brian Cabral
AN OMNISTEREOSCOPIC VIDEO PIPELINE FOR CAPTURE AND DISPLAY OF REAL-WORLD VR
PHASENET FOR VIDEO FRAME INTERPOLATION
[Paper]
Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus Gross, Christopher Schroers
A FULLY PROGRESSIVE APPROACH TO SINGLE-IMAGE SUPER-RESOLUTION
[Paper]
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
LEARNING VIDEO OBJECT SEGMENTATION FROM STATIC IMAGES
DEPTH FROM GRADIENTS IN DENSE LIGHT FIELDS FOR OBJECT RECONSTRUCTION
International Conference on 3D Vision (3DV), 2016
Best paper award
BILATERAL SPACE VIDEO SEGMENTATION
EFFICIENT 3D OBJECT SEGMENTATION FROM DENSELY SAMPLED LIGHT FIELDS WITH APPLICATIONS TO 3D RECONSTRUCTION
SAMPLING BASED SCENE-SPACE VIDEO PROCESSING
PANORAMIC VIDEO FROM UNSTRUCTURED CAMERA ARRAYS
SCENE RECONSTRUCTION FROM HIGH SPATIO-ANGULAR RESOLUTION LIGHT FIELDS
