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Computer Science

D-Index
60
Citations
13135
World Ranking
3268
National Ranking
152

Overview

Bodo Rosenhahn is affiliated with the University of Hannover in Germany. Their research focuses primarily on computer science and engineering, with significant contributions in the subfields of computer vision and pattern recognition, artificial intelligence, molecular biology, biomedical engineering, and electrical and electronic engineering.

The scientist's recent published papers include:

  • Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • RelTR: Relation Transformer for Scene Graph Generation, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Asymmetric Student-Teacher Networks for Industrial Anomaly Detection, 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Text to Image Generation with Semantic-Spatial Aware GAN, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Spatial-Temporal Transformer for Dynamic Scene Graph Generation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Their frequent co-authors include Michael Ying Yang, Wentong Liao, Marco Rudolph, Frederik Schubert, and Yuren Cong.

Common publication venues for Rosenhahn's work are:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Games

The main topics covered in Rosenhahn's research include:

  • Anomaly Detection Techniques and Applications
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Advanced Neural Network Applications
  • Generative Adversarial Networks and Image Synthesis
  • Video Surveillance and Tracking Methods
  • Advanced Image and Video Retrieval Techniques

Best Publications

  • Recovering Accurate {3D} Human Pose in the Wild Using {IMUs} and a Moving Camera

    Timo von Marcard;Roberto Henschel;Michael J. Black;Bodo Rosenhahn

  • Motion capture using joint skeleton tracking and surface estimation

    Juergen Gall;Carsten Stoll;Edilson de Aguiar;Christian Theobalt

  • A Statistical Model of Human Pose and Body Shape

    Nils Hasler;Carsten Stoll;Martin Sunkel;Bodo Rosenhahn

  • Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

    Marco Rudolph;Bastian Wandt;Bodo Rosenhahn

  • Automatic human model generation

    Bodo Rosenhahn;Lei He;Reinhard Klette

  • Optimization and Filtering for Human Motion Capture

    Juergen Gall;Bodo Rosenhahn;Thomas Brox;Hans-Peter Seidel

  • Learning an Image-Based Motion Context for Multiple People Tracking

    Laura Leal-Taixé;Michele Fenzi;Alina Kuznetsova;Bodo Rosenhahn

  • Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

    T. von Marcard;B. Rosenhahn;M. J. Black;G. Pons-Moll

  • Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection.

    Marco Rudolph;Tom Wehrbein;Bodo Rosenhahn;Bastian Wandt

  • RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation

    Bastian Wandt;Bodo Rosenhahn

  • Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker

    Laura Leal-Taixe;Gerard Pons-Moll;Bodo Rosenhahn

  • RelTR: Relation Transformer for Scene Graph Generation

    Unknown

  • Real-Time Sign Language Recognition Using a Consumer Depth Camera

    Alina Kuznetsova;Laura Leal-Taixe;Bodo Rosenhahn

  • Complementary Optic Flow

    Henning Zimmer;Andrés Bruhn;Joachim Weickert;Levi Valgaerts

  • Combined Region and Motion-Based 3D Tracking of Rigid and Articulated Objects

    T. Brox;B. Rosenhahn;J. Gall;D. Cremers

  • Human Pose Estimation from Video and IMUs

    Timo von Marcard;Gerard Pons-Moll;Bodo Rosenhahn

  • Multisensor-fusion for 3D full-body human motion capture

    Gerard Pons-Moll;Andreas Baak;Thomas Helten;Meinard Muller

  • Text to Image Generation with Semantic-Spatial Aware GAN.

    Kai Hu;Wentong Liao;Michael Ying Yang;Bodo Rosenhahn

  • Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking

    Bodo Rosenhahn;Thomas Brox;Joachim Weickert

  • Fusion of Head and Full-Body Detectors for Multi-object Tracking

    Roberto Henschel;Laura Leal-Taixe;Daniel Cremers;Bodo Rosenhahn

  • Markerless Motion Capture with unsynchronized moving cameras

    Nils Hasler;Bodo Rosenhahn;Thorsten Thormahlen;Michael Wand

  • Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

    Timo von Marcard;Bodo Rosenhahn;Michael J. Black;Gerard Pons-Moll

Frequent Co-Authors

Michael Ying Yang
Michael Ying Yang University of Bath
Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics
Thomas Brox
Thomas Brox University of Freiburg
Jörn Ostermann
Jörn Ostermann University of Hannover
Laura Leal-Taixé
Laura Leal-Taixé Technical University of Munich
Gerald Sommer
Gerald Sommer Kiel University
Reinhard Klette
Reinhard Klette Auckland University of Technology
Daniel Cremers
Daniel Cremers Technical University of Munich
Joachim Weickert
Joachim Weickert Saarland University
Gerard Pons-Moll
Gerard Pons-Moll University of Tübingen

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