M
Michael Stengel
Researcher at Nvidia
Publications - 28
Citations - 836
Michael Stengel is an academic researcher from Nvidia. The author has contributed to research in topics: Rendering (computer graphics) & Eye tracking. The author has an hindex of 12, co-authored 25 publications receiving 579 citations. Previous affiliations of Michael Stengel include Otto-von-Guericke University Magdeburg & Delft University of Technology.
Papers
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Journal ArticleDOI
Near-Eye Display and Tracking Technologies for Virtual and Augmented Reality
George Alex Koulieris,Kaan Akşit,Michael Stengel,Rafal Mantiuk,Katerina Mania,Christian Richardt +5 more
TL;DR: This state‐of‐the‐art report investigates the background theory of perception and vision as well as the latest advancements in display engineering and tracking technologies involved in near‐eye displays.
Journal ArticleDOI
Foveated AR: dynamically-foveated augmented reality display
Jonghyun Kim,Youngmo Jeong,Michael Stengel,Kaan Akşit,Rachel Albert,Ben Boudaoud,Trey Greer,Joohwan Kim,Ward Lopes,Zander Majercik,Peter Shirley,Josef Spjut,Morgan McGuire,David Luebke +13 more
TL;DR: This work presents a near-eye augmented reality display with resolution and focal depth dynamically driven by gaze tracking, and shows prototypes supporting 30, 40 and 60 cpd foveal resolution at a net 85° × 78° field of view per eye.
Proceedings ArticleDOI
NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation
Joohwan Kim,Michael Stengel,Alexander Majercik,Shalini De Mello,David Dunn,Samuli Laine,Morgan McGuire,David Luebke +7 more
TL;DR: This work creates a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions, and trains neural networks performing with sub-millisecond latency.
Journal ArticleDOI
Adaptive Image-Space Sampling for Gaze-Contingent Real-time Rendering
TL;DR: This work proposes an algorithm that only shades visible features of the image while cost‐effectively interpolating the remaining features without affecting perceived quality, and introduces a sampling scheme that incorporates multiple aspects of the human visual system: acuity, eye motion, contrast, and brightness adaptation.
Journal ArticleDOI
Garment Replacement in Monocular Video Sequences
TL;DR: A semi-automatic approach to exchange the clothes of an actor for arbitrary virtual garments in conventional monocular video footage as a postprocess using a parameterized body model, which serves as an animated mannequin for simulating the virtual garment.