World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
17487
World Ranking
7374
National Ranking
3214

Angjoo Kanazawa 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 Angjoo Kanazawa 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: 89 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Angjoo Kanazawa 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 Angjoo Kanazawa 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Angjoo Kanazawa is affiliated with the University of California, Berkeley in the United States. Their research primarily spans the field of Computer Science with a strong focus on Computer Vision and Pattern Recognition.

Their scholarly contributions include a significant number of publications, with frequent appearances in prominent venues. Key publication venues for Kanazawa's work include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • ACM Transactions on Graphics
  • Computer Graphics Forum

Kanazawa's research covers various subfields in Computer Science, such as:

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Computational Mechanics
  • Control and Systems Engineering
  • Human-Computer Interaction

Among the main topics they have investigated are:

  • Advanced Vision and Imaging
  • Human Pose and Action Recognition
  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Generative Adversarial Networks and Image Synthesis
  • Human Motion and Animation
  • Video Surveillance and Tracking Methods

Their recent notable papers include:

  • "Plenoxels: Radiance Fields without Neural Networks," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "PlenOctrees for Real-time Rendering of Neural Radiance Fields," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "AI Choreographer: Music Conditioned 3D Dance Generation with AIST++," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "AMP," 2021, ACM Transactions on Graphics
  • "Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequent collaborators in their research include:

  • Matthew Tancik
  • Jitendra Malik
  • Georgios Pavlakos
  • Noah Snavely
  • Vickie Ye

Best Publications

  • Plenoxels: Radiance Fields without Neural Networks

    Unknown

  • End-to-End Recovery of Human Shape and Pose

    Angjoo Kanazawa;Michael J. Black;David W. Jacobs;Jitendra Malik

  • Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

    Federica Bogo;Angjoo Kanazawa;Christoph Lassner;Christoph Lassner;Peter V. Gehler;Peter V. Gehler

  • pixelNeRF: Neural Radiance Fields from One or Few Images

    Alex Yu;Vickie Ye;Matthew Tancik;Angjoo Kanazawa

  • PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

    Shunsuke Saito;Zeng Huang;Ryota Natsume;Shigeo Morishima

  • PlenOctrees for Real-Time Rendering of Neural Radiance Fields

    Alex Yu;Ruilong Li;Matthew Tancik;Hao Li

  • Learning Category-Specific Mesh Reconstruction from Image Collections

    Angjoo Kanazawa;Shubham Tulsiani;Alexei A. Efros;Jitendra Malik

  • Learning 3D Human Dynamics From Video

    Angjoo Kanazawa;Jason Y. Zhang;Panna Felsen;Jitendra Malik

  • K-Planes: Explicit Radiance Fields in Space, Time, and Appearance

    Unknown

  • Nerfstudio: A Modular Framework for Neural Radiance Field Development

    Unknown

  • AI Choreographer: Music Conditioned 3D Dance Generation With AIST++

    Ruilong Li;Shan Yang;David A. Ross;Angjoo Kanazawa

  • 3D Menagerie: Modeling the 3D Shape and Pose of Animals

    Silvia Zuffi;Angjoo Kanazawa;David W. Jacobs;Michael J. Black

  • SfSNet: Learning Shape, Reflectance and Illuminance of Faces 'in the Wild'

    Soumyadip Sengupta;Angjoo Kanazawa;Carlos D. Castillo;David W. Jacobs

  • AMP: adversarial motion priors for stylized physics-based character control

    Xue Bin Peng;Ze Ma;Pieter Abbeel;Sergey Levine

  • Dog breed classification using part localization

    Jiongxin Liu;Angjoo Kanazawa;David Jacobs;Peter Belhumeur

  • LERF: Language Embedded Radiance Fields

    Unknown

  • SFV: reinforcement learning of physical skills from videos

    Xue Bin Peng;Angjoo Kanazawa;Jitendra Malik;Pieter Abbeel

  • Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

    Unknown

  • PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

    Shunsuke Saito;Zeng Huang;Ryota Natsume;Shigeo Morishima

  • Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

    Xue Bin Peng;Angjoo Kanazawa;Sam Toyer;Pieter Abbeel

  • WarpNet: Weakly Supervised Matching for Single-View Reconstruction

    Angjoo Kanazawa;David W. Jacobs;Manmohan Chandraker

  • Locally Scale-Invariant Convolutional Neural Networks

    Angjoo Kanazawa;Abhishek Sharma;David W. Jacobs

  • Lions and Tigers and Bears: Capturing Non-rigid, 3D, Articulated Shape from Images

    Silvia Zuffi;Angjoo Kanazawa;Michael J. Black

  • Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”

    Silvia Zuffi;Angjoo Kanazawa;Tanya Berger-Wolf;Michael Black

  • Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

    Jason Y. Zhang;Sam Pepose;Hanbyul Joo;Deva Ramanan

  • Shape and Viewpoint Without Keypoints

    Shubham Goel;Angjoo Kanazawa;Jitendra Malik

Frequent Co-Authors

Jitendra Malik
Jitendra Malik University of California, Berkeley
David W. Jacobs
David W. Jacobs University of Maryland, College Park
Michael J. Black
Michael J. Black Max Planck Institute for Intelligent Systems
Noah Snavely
Noah Snavely Cornell University
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Sergey Levine
Sergey Levine University of California, Berkeley
Jiajun Wu
Jiajun Wu Stanford University
Hao Li
Hao Li University of California, Berkeley
Ronen Basri
Ronen Basri Weizmann Institute of Science

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

As you consider studying Computer Science in the USA, it's important to explore related fields and alternative online degree options. The rapid growth of technology has increased demand for professionals in STEM fields, making online degrees more accessible and respected by employers.

For students interested in applied sciences, an online degree for mechanical engineering offers the flexibility to learn key engineering principles from anywhere. If you are attracted to foundational sciences, earning an online physics bachelor's degree can open doors to research and high-tech industries.

Data remains central to innovation. Accredited data science programs prepare students for fast-growing careers in analytics, artificial intelligence, and machine learning. Interested in electronics and power systems? Understanding electrical engineering degree online admissions can help you launch a career in this versatile field.

Each of these online degrees offers specialized skills and flexible pathways, empowering students to adapt to evolving technology careers.

Best Scientists Citing Angjoo Kanazawa

Trending Scientists

Recently Published Articles