D-Index & Metrics Best Publications

Michael Zollhöfer

Facebook (United States)
United States

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 42 Citations 9,281 98 World Ranking 5186 National Ranking 2553

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study Michael Zollhöfer is best known for:

Computer graphics (images)
3D computer graphics
Algorithm

In his research on the topic of Parallelizable manifold, State (computer science) is strongly related with Algorithm. His State (computer science) study frequently intersects with other fields, such as Algorithm. Michael Zollhöfer performs multidisciplinary study on Computer graphics (images) and 3D reconstruction in his works. He performs integrative 3D reconstruction and Computer graphics (images) research in his work. Michael Zollhöfer frequently studies issues relating to Retargeting and Artificial intelligence. Computer vision and Monocular are frequently intertwined in his study. His Rendering (computer graphics) study typically links adjacent topics like View synthesis. Much of his study explores View synthesis relationship to Rendering (computer graphics). Michael Zollhöfer integrates Programming language and Expression (computer science) in his research.

His most cited work include:

Deferred neural rendering (686 citations)
Real-time 3D reconstruction at scale using voxel hashing (622 citations)
Deep video portraits (395 citations)

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

As part of his studies on Artificial intelligence, Michael Zollhöfer often connects relevant subjects like Pattern recognition (psychology). The study of Computer vision is intertwined with the study of Frame rate in a number of ways. Michael Zollhöfer performs integrative Computer graphics (images) and Human–computer interaction research in his work. In his works, Michael Zollhöfer conducts interdisciplinary research on Human–computer interaction and Computer graphics (images). His study connects View synthesis and Rendering (computer graphics). His research on View synthesis often connects related topics like Rendering (computer graphics). He undertakes interdisciplinary study in the fields of Face (sociological concept) and Social science through his research. Michael Zollhöfer combines Social science and Face (sociological concept) in his research. Michael Zollhöfer combines Algorithm and Computer vision in his research.

Michael Zollhöfer most often published in these fields:

Artificial intelligence (94.12%)
Computer vision (88.24%)
Computer graphics (images) (74.51%)

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.

Best Publications

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

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

1084 Citations

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)

811 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)

559 Citations

Deferred neural rendering: image synthesis using neural textures

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

538 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)

438 Citations

Deep video portraits

Hyeongwoo Kim;Pablo Garrido;Ayush Tewari;Weipeng Xu.
international conference on computer graphics and interactive techniques (2018)

383 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)

363 Citations

DeepVoxels: Learning Persistent 3D Feature Embeddings

Vincent Sitzmann;Justus Thies;Felix Heide;Matthias NieBner.
computer vision and pattern recognition (2019)

311 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)