P
Peter Eisert
Researcher at Humboldt University of Berlin
Publications - 241
Citations - 4669
Peter Eisert is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 30, co-authored 216 publications receiving 4020 citations. Previous affiliations of Peter Eisert include Stanford University & VTT Technical Research Centre of Finland.
Papers
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Journal ArticleDOI
Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet
Jorge Posada,Carlos Toro,Iñigo Barandiaran,David Oyarzun,Didier Stricker,Raffaele De Amicis,Eduardo B. Pinto,Peter Eisert,Jürgen Döllner,Ivan Vallarino +9 more
TL;DR: This article positions visual computing in its intrinsic crucial role for Industrie 4.0 and provides a general, broad overview and points out specific directions and scenarios for future research.
Proceedings ArticleDOI
3D Video and Free Viewpoint Video - Technologies, Applications and MPEG Standards
Aljoscha Smolic,Karsten Mueller,Philipp Merkle,Christoph Fehn,Peter Kauff,Peter Eisert,Thomas Wiegand +6 more
TL;DR: The conclusion is that the necessary technology including standard media formats for 3D and free viewpoint is available or will be available in the near future, and that there is a clear demand from industry and user side for such applications.
Journal ArticleDOI
Analyzing facial expressions for virtual conferencing
Peter Eisert,Bernd Girod +1 more
TL;DR: The authors present a model-based algorithm that estimates 3D motion and facial expressions from 2D image sequences showing head and shoulder scenes typical of video telephone and teleconferencing applications.
Journal ArticleDOI
Platform for distributed 3D gaming
Audrius Jurgelionis,Philipp Fechteler,Peter Eisert,Francesco Bellotti,Haggai David,Jukka-Pekka Laulajainen,R Carmichael,Vassilis Poulopoulos,Arto Laikari,P. Perala,A. De Gloria,Christos Bouras +11 more
TL;DR: Simultaneous execution of video games on a central server and a novel streaming approach of the 3D graphics output to multiple end devices enable the access of games on low cost set top boxes and handheld devices that natively lack the power of executing a game with high-quality graphical output.
Book ChapterDOI
Detection of Face Morphing Attacks by Deep Learning
TL;DR: An automatic morphing pipeline is presented to generate morphing attacks, train neural networks based on this data and analyze their accuracy, and the accuracy of different well-known network architectures are compared.