The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Face, Computer graphics and RGB color model. Artificial intelligence is frequently linked to Computer graphics in his study. The study incorporates disciplines such as Robustness and Graphics in addition to Computer vision.
His study explores the link between Face and topics such as Iterative reconstruction that cross with problems in Regularization, Pattern recognition and Feature extraction. His Computer graphics research is multidisciplinary, incorporating elements of Motion, Geometric shape, Match moving, Distance transform and Representation. Michael Zollhöfer has researched RGB color model in several fields, including Video tracking and Tracking.
Michael Zollhöfer spends much of his time researching Artificial intelligence, Computer vision, RGB color model, Face and Monocular. His study brings together the fields of Pattern recognition and Artificial intelligence. His research integrates issues of Facial expression and Computer graphics, Computer graphics in his study of Computer vision.
The Facial expression study combines topics in areas such as Animation and Eye tracking. His RGB color model research integrates issues from Augmented reality, Tracking, Representation and Distance transform. His work in Face tackles topics such as Color image which are related to areas like Image formation and Autoencoder.
Michael Zollhöfer mostly deals with Artificial intelligence, Computer vision, Graphics, Rendering and Artificial neural network. His research links Pattern recognition with Artificial intelligence. Monocular is the focus of his Computer vision research.
His Graphics research focuses on subjects like View synthesis, which are linked to Metaverse, Human–computer interaction and Monocular video. His Rendering research is multidisciplinary, incorporating perspectives in Animation, Voxel and Computer graphics. While the research belongs to areas of Artificial neural network, Michael Zollhöfer spends his time largely on the problem of Computer animation, intersecting his research to questions surrounding Optimization problem.
His primary areas of study are Artificial intelligence, Computer vision, Face, Graphics and Iterative reconstruction. His work on Autoencoder as part of his general Artificial intelligence study is frequently connected to Parametric statistics, thereby bridging the divide between different branches of science. His study in the fields of Monocular and RGB color model under the domain of Computer vision overlaps with other disciplines such as Radiance.
His biological study spans a wide range of topics, including Volume rendering, Representation, Avatar and Contrast. His work in Graphics addresses subjects such as Metaverse, which are connected to disciplines such as Augmented reality and Rendering. His research in Iterative reconstruction intersects with topics in Image formation, Color image and Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Real-time 3D reconstruction at scale using voxel hashing
Matthias Nießner;Michael Zollhöfer;Shahram Izadi;Marc Stamminger.
international conference on computer graphics and interactive techniques (2013)
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
Justus Thies;Michael Zollhofer;Marc Stamminger;Christian Theobalt.
computer vision and pattern recognition (2016)
Deferred neural rendering: image synthesis using neural textures
Justus Thies;Michael Zollhöfer;Matthias Nießner.
ACM Transactions on Graphics (2019)
BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
Angela Dai;Matthias Nießner;Michael Zollhöfer;Shahram Izadi.
ACM Transactions on Graphics (2017)
Real-time non-rigid reconstruction using an RGB-D camera
Michael Zollhöfer;Matthias Nießner;Shahram Izadi;Christoph Rehmann.
international conference on computer graphics and interactive techniques (2014)
Face2Face: real-time face capture and reenactment of RGB videos
Justus Thies;Michael Zollhöfer;Marc Stamminger;Christian Theobalt.
Communications of The ACM (2018)
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Ayush Tewari;Michael Zollhofer;Hyeongwoo Kim;Pablo Garrido.
international conference on computer vision (2017)
Real-time expression transfer for facial reenactment
Justus Thies;Michael Zollhöfer;Matthias Nießner;Levi Valgaerts.
international conference on computer graphics and interactive techniques (2015)
Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz
Ayush Tewari;Michael Zollhofer;Pablo Garrido;Florian Bernard.
computer vision and pattern recognition (2018)
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction
Matthias Innmann;Michael Zollhöfer;Matthias Nießner;Christian Theobalt.
european conference on computer vision (2016)
Profile was last updated on December 6th, 2021.
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