World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
102
Citations
51267
World Ranking
333
National Ranking
181

Jan Kautz publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jan Kautz sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 371 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jan Kautz D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jan Kautz sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 102 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jan Kautz is affiliated with Nvidia in the United States and has contributed extensively to the field of computer science, with a particular focus on computer vision and pattern recognition.

The recent publications associated with Jan Kautz include:

  • NVAE: A Deep Hierarchical Variational Autoencoder, 2020, arXiv (Cornell University)
  • GroupViT: Semantic Segmentation Emerges from Text Supervision, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • A-ViT: Adaptive Tokens for Efficient Vision Transformer, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Video-to-Video Synthesis, 2025, arXiv (Cornell University)
  • GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The frequent co-authors collaborating with Jan Kautz are:

  • Pavlo Molchanov
  • Sifei Liu
  • Zhiding Yu
  • Hongxu Yin
  • Arash Vahdat

Jan Kautz has published across several notable venues, including:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • International Journal of Computer Vision
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lecture notes in computer science

The main fields of research include computer science with a specialization in subfields such as:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Computational Mechanics
  • Control and Systems Engineering
  • Aerospace Engineering

The primary topics of work associated with Jan Kautz cover a range of areas, including:

  • Advanced Vision and Imaging
  • Human Pose and Action Recognition
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Generative Adversarial Networks and Image Synthesis
  • 3D Shape Modeling and Analysis

Best Publications

  • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

    Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Andrew Tao

  • Loss Functions for Image Restoration With Neural Networks

    Hang Zhao;Orazio Gallo;Iuri Frosio;Jan Kautz

  • Multimodal Unsupervised Image-to-Image Translation

    Xun Huang;Ming-Yu Liu;Serge J. Belongie;Jan Kautz

  • PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

    Deqing Sun;Xiaodong Yang;Ming-Yu Liu;Jan Kautz

  • Unsupervised Image-to-Image Translation Networks

    Ming-Yu Liu;Thomas M. Breuel;Jan Kautz

  • Pruning Convolutional Neural Networks for Resource Efficient Inference

    Pavlo Molchanov;Stephen Tyree;Tero Karras;Timo Aila

  • Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments

    Peter-Pike Sloan;Jan Kautz;John Snyder

  • Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography

    T. Mertens;J. Kautz;F. Van Reeth

  • MoCoGAN: Decomposing Motion and Content for Video Generation

    Sergey Tulyakov;Ming-Yu Liu;Xiaodong Yang;Jan Kautz

  • Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

    Huaizu Jiang;Deqing Sun;Varan Jampani;Ming-Hsuan Yang

  • Joint Discriminative and Generative Learning for Person Re-Identification

    Zhedong Zheng;Xiaodong Yang;Zhiding Yu;Liang Zheng

  • SPLATNet: Sparse Lattice Networks for Point Cloud Processing

    Hang Su;Varun Jampani;Deqing Sun;Subhransu Maji

  • Importance Estimation for Neural Network Pruning

    Pavlo Molchanov;Arun Mallya;Stephen Tyree;Iuri Frosio

  • Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks

    Pavlo Molchanov;Xiaodong Yang;Shalini Gupta;Kihwan Kim

  • Few-Shot Unsupervised Image-to-Image Translation

    Ming-Yu Liu;Xun Huang;Arun Mallya;Tero Karras

  • Exposure Fusion

    T. Mertens;J. Kautz;F. Van Reeth

  • Hand gesture recognition with 3D convolutional neural networks

    Pavlo Molchanov;Shalini Gupta;Kihwan Kim;Jan Kautz

  • GroupViT: Semantic Segmentation Emerges from Text Supervision

    Unknown

  • Video-to-Video Synthesis

    Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Guilin Liu

  • Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion

    Hongxu Yin;Pavlo Molchanov;Jose M. Alvarez;Zhizhong Li

  • NVAE: A Deep Hierarchical Variational Autoencoder

    Arash Vahdat;Jan Kautz

Frequent Co-Authors

Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics
Ming-Yu Liu
Ming-Yu Liu Nvidia (United States)
Jinwei Gu
Jinwei Gu Chinese University of Hong Kong
Xiaodong Yang
Xiaodong Yang Nvidia (United Kingdom)
Pavlo Molchanov
Pavlo Molchanov Nvidia (United States)
Ming-Hsuan Yang
Ming-Hsuan Yang University of California, Merced
Hendrik P. A. Lensch
Hendrik P. A. Lensch University of Tübingen
Michael Goesele
Michael Goesele Technical University of Darmstadt
Deqing Sun
Deqing Sun Google (United States)
Christian Theobalt
Christian Theobalt Max Planck Institute for Informatics

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Expanding your education beyond a traditional Computer Science degree offers numerous opportunities. Many students and professionals explore degrees you can get online that pay well, providing flexibility and a strong return on investment. These programs are ideal for individuals seeking to quickly transition into high-demand fields or boost their current careers.

As technology rapidly evolves, interest in artificial intelligence is soaring. You can now pursue specialized ai degrees affordably online, unlocking career paths in machine learning, robotics, and automation. These roles are not only intellectually rewarding but also increasingly sought after by employers.

Choosing the right college major can have a long-lasting impact on your future. For those still deciding, reviewing the majors in college that lead to successful outcomes is a smart move. This can guide you toward disciplines that match your interests and market demands.

Additionally, if you’re considering further education, there are easy masters programs to get into online. These programs can open doors to advancement while balancing work or other commitments. Exploring these diverse educational pathways ensures you remain adaptable and competitive in a changing job market.

Best Scientists Citing Jan Kautz

Trending Scientists

Recently Published Articles