ABOUT
Alexander Sorkine-Hornung
Research Scientist and XR Systems Architect at the interface of machine perception, graphics and AI. Experienced in incubating and productizing novel user experiences and technologies from 0 to 1. Technical lead in shipping Meta’s Mixed Reality product line.
Scholar statistics: 100+ published scientific papers, 80+ patents, 14k+ citations, h‑index 47.

SHORT CV
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
