H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 41 Citations 8,893 82 World Ranking 4383 National Ranking 2193

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Computer graphics

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.

His most cited work include:

  • Face2Face: Real-Time Face Capture and Reenactment of RGB Videos (682 citations)
  • Real-time 3D reconstruction at scale using voxel hashing (566 citations)
  • BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration (333 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (87.00%)
  • Computer vision (78.00%)
  • RGB color model (26.00%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (87.00%)
  • Computer vision (78.00%)
  • Graphics (13.00%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • State of the Art on Neural Rendering (54 citations)
  • StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images (48 citations)
  • DeepCap: Monocular Human Performance Capture Using Weak Supervision (31 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Computer graphics

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.

Top Publications

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)

598 Citations

Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

Justus Thies;Michael Zollhofer;Marc Stamminger;Christian Theobalt.
computer vision and pattern recognition (2016)

582 Citations

Deferred neural rendering: image synthesis using neural textures

Justus Thies;Michael Zollhöfer;Matthias Nießner.
ACM Transactions on Graphics (2019)

409 Citations

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)

344 Citations

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)

343 Citations

Face2Face: real-time face capture and reenactment of RGB videos

Justus Thies;Michael Zollhöfer;Marc Stamminger;Christian Theobalt.
Communications of The ACM (2018)

241 Citations

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)

211 Citations

Real-time expression transfer for facial reenactment

Justus Thies;Michael Zollhöfer;Matthias Nießner;Levi Valgaerts.
international conference on computer graphics and interactive techniques (2015)

203 Citations

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)

189 Citations

VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction

Matthias Innmann;Michael Zollhöfer;Matthias Nießner;Christian Theobalt.
european conference on computer vision (2016)

187 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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