World's Best Scientists 2026 revealed!
Gerard Pons-Moll

Gerard Pons-Moll

D-Index & Metrics

Computer Science

D-Index
60
Citations
22667
World Ranking
3172
National Ranking
151

Gerard Pons-Moll 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 Gerard Pons-Moll 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: 118 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Gerard Pons-Moll 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 Gerard Pons-Moll 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Gerard Pons-Moll is affiliated with the University of Tübingen in Germany. Their research spans several main fields, primarily Computer Science and Engineering, with a focus on numerous subfields including Computer Vision and Pattern Recognition, Computational Mechanics, Control and Systems Engineering, Computer Graphics and Computer-Aided Design, and Human-Computer Interaction.

The scientist's work concentrates on a range of main topics such as Human Pose and Action Recognition, 3D Shape Modeling and Analysis, Advanced Vision and Imaging, Human Motion and Animation, Computer Graphics and Visualization Techniques, Generative Adversarial Networks and Image Synthesis, and Video Surveillance and Tracking Methods.

Their recent publication record includes several papers across prominent venues. Notable works include:

  • D-NeRF: neural radiance fields for dynamic scenes, 2021, published in UPCommons (Polytechnic University of Catalonia)
  • SMPLicit: Topology-aware generative model for clothed people, 2021, DIGITAL.CSIC (Spanish National Research Council (CSIC))
  • BEHAVE: Dataset and Method for Tracking Human Object Interactions, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SelfPose: 3D Egocentric Pose Estimation From a Headset Mounted Camera, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation, 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Gerard Pons-Moll frequently collaborates with a core group of researchers. These frequent co-authors include Bharat Lal Bhatnagar, Christian Theobalt, Jan Eric Lenssen, Julian Chibane, and Riccardo Marin.

Their publications are predominantly found in venues such as arXiv (Cornell University), Lecture Notes in Computer Science, IEEE Transactions on Pattern Analysis and Machine Intelligence, UPCommons (Polytechnic University of Catalonia), and the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Best Publications

  • SMPL: A Skinned Multi-Person Linear Model

    Unknown

  • SMPL: a skinned multi-person linear model

    Matthew Loper;Naureen Mahmood;Javier Romero;Gerard Pons-Moll

  • D-NeRF: Neural Radiance Fields for Dynamic Scenes

    Albert Pumarola;Enric Corona;Gerard Pons-Moll;Francesc Moreno-Noguer

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

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

  • AMASS: Archive of Motion Capture As Surface Shapes

    Naureen Mahmood;Nima Ghorbani;Nikolaus F. Troje;Gerard Pons-Moll

  • Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation

    Mohamed Omran;Christoph Lassner;Gerard Pons-Moll;Peter Gehler

  • ClothCap: seamless 4D clothing capture and retargeting

    Gerard Pons-Moll;Sergi Pujades;Sonny Hu;Michael J. Black

  • Video Based Reconstruction of 3D People Models

    Thiemo Alldieck;Marcus Magnor;Weipeng Xu;Christian Theobalt

  • Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

    Julian Chibane;Thiemo Alldieck;Gerard Pons-Moll

  • XNect: real-time multi-person 3D motion capture with a single RGB camera

    Dushyant Mehta;Oleksandr Sotnychenko;Franziska Mueller;Weipeng Xu

  • Single-Shot Multi-person 3D Pose Estimation from Monocular RGB

    Dushyant Mehta;Oleksandr Sotnychenko;Franziska Mueller;Weipeng Xu

  • Multi-Garment Net: Learning to Dress 3D People From Images

    Bharat Bhatnagar;Garvita Tiwari;Christian Theobalt;Gerard Pons-Moll

  • Dynamic FAUST: Registering Human Bodies in Motion

    Federica Bogo;Javier Romero;Gerard Pons-Moll;Michael J. Black

  • Learning to Dress 3D People in Generative Clothing

    Qianli Ma;Jinlong Yang;Anurag Ranjan;Sergi Pujades

  • Dyna: a model of dynamic human shape in motion

    Gerard Pons-Moll;Javier Romero;Naureen Mahmood;Michael J. Black

  • Learning to Reconstruct People in Clothing From a Single RGB Camera

    Thiemo Alldieck;Marcus Magnor;Bharat Lal Bhatnagar;Christian Theobalt

  • Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time

    Yinghao Huang;Manuel Kaufmann;Emre Aksan;Michael J. Black

  • Tex2Shape: Detailed Full Human Body Geometry From a Single Image

    Thiemo Alldieck;Gerard Pons-Moll;Christian Theobalt;Marcus Magnor

  • Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences

    Chao Zhang;Sergi Pujades;Michael Black;Gerard Pons-Moll

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

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

  • DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

    Tao Yu;Zerong Zheng;Kaiwen Guo;Jianhui Zhao

  • Neural Unsigned Distance Fields for Implicit Function Learning

    Julian Chibane;Mohamad Aymen mir;Gerard Pons-Moll

  • 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

Christian Theobalt
Christian Theobalt Max Planck Institute for Informatics
Michael J. Black
Michael J. Black Max Planck Institute for Intelligent Systems
Bodo Rosenhahn
Bodo Rosenhahn University of Hannover
Hans-Peter Seidel
Hans-Peter Seidel Max Planck Institute for Informatics
Laura Leal-Taixé
Laura Leal-Taixé Technical University of Munich
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Marcus Magnor
Marcus Magnor Technische Universität Braunschweig
Qionghai Dai
Qionghai Dai Tsinghua University
Javier Romero
Javier Romero Facebook (United States)
Mario Fritz
Mario Fritz Helmholtz Center for Information Security

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