D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index 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. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 8,339 52 World Ranking 8884 National Ranking 421

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Javier Romero mainly focuses on Artificial intelligence, Computer vision, Graphics, Polygon mesh and Image segmentation. The various areas that Javier Romero examines in his Artificial intelligence study include Soft tissue and Human–computer interaction. His studies deal with areas such as Artificial neural network and Benchmark as well as Computer vision.

His Graphics research is multidisciplinary, incorporating perspectives in Linear model and Rendering. His Polygon mesh research includes elements of Leverage, 2D to 3D conversion, Image, Single image and Human body. His Image segmentation research incorporates elements of Closing, Machine learning, Discriminative model and Data mining.

His most cited work include:

  • SMPL: a skinned multi-person linear model (896 citations)
  • Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image (749 citations)
  • A Simple Yet Effective Baseline for 3d Human Pose Estimation (488 citations)

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

Javier Romero spends much of his time researching Artificial intelligence, Computer vision, Pose, Polygon mesh and Robot. His is doing research in Motion, Object, Motion capture, Optical flow and Deep learning, both of which are found in Artificial intelligence. His research brings together the fields of Convolutional neural network and Computer vision.

His study focuses on the intersection of Pose and fields such as Robotics with connections in the field of Robustness and Humanoid robot. His work deals with themes such as Algorithm and Rendering, which intersect with Polygon mesh. His work on Human–robot interaction and Visual servoing as part of general Robot research is often related to GRASP, thus linking different fields of science.

He most often published in these fields:

  • Artificial intelligence (77.78%)
  • Computer vision (59.72%)
  • Pose (16.67%)

What were the highlights of his more recent work (between 2018-2020)?

  • Artificial intelligence (77.78%)
  • Deep learning (8.33%)
  • Polygon mesh (13.89%)

In recent papers he was focusing on the following fields of study:

Javier Romero focuses on Artificial intelligence, Deep learning, Polygon mesh, Focus and Pattern recognition. His work on Algorithm expands to the thematically related Artificial intelligence. His Algorithm research integrates issues from Artificial neural network, Point cloud and Representation.

His Motion capture research focuses on Real image and how it connects with Pattern recognition. His Computer graphics study integrates concerns from other disciplines, such as Pixel and Rendering. His Facsimile research incorporates Computer vision, Bar and Image.

Between 2018 and 2020, his most popular works were:

  • FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second (21 citations)
  • Efficient Learning on Point Clouds With Basis Point Sets (20 citations)
  • Learning Multi-human Optical Flow (11 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of investigation include Artificial intelligence, Focus, Facsimile, Computer vision and Iterative reconstruction. His study in the field of Motion capture is also linked to topics like Discretization. Javier Romero combines subjects such as Optical flow, Real image and Pattern recognition with his study of Motion capture.

His Discretization study overlaps with Occupancy grid mapping, Algorithm, Deep learning, Surface reconstruction and Representation. His Occupancy grid mapping research incorporates a variety of disciplines, including Artificial neural network, Polygon mesh and Point cloud. Javier Romero merges many fields, such as Facsimile and Robustness, in his writings.

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

SMPL: a skinned multi-person linear model

Matthew Loper;Naureen Mahmood;Javier Romero;Gerard Pons-Moll.
international conference on computer graphics and interactive techniques (2015)

727 Citations

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.
european conference on computer vision (2016)

558 Citations

A Simple Yet Effective Baseline for 3d Human Pose Estimation

Julieta Martinez;Rayat Hossain;Javier Romero;James J. Little.
international conference on computer vision (2017)

488 Citations

FAUST: Dataset and Evaluation for 3D Mesh Registration

Federica Bogo;Javier Romero;Matthew Loper;Michael J. Black.
computer vision and pattern recognition (2014)

486 Citations

The GRASP Taxonomy of Human Grasp Types

Thomas Feix;Javier Romero;Heinz-Bodo Schmiedmayer;Aaron M. Dollar.
IEEE Transactions on Human-Machine Systems (2016)

470 Citations

Learning from Synthetic Humans

Gul Varol;Javier Romero;Xavier Martin;Naureen Mahmood.
computer vision and pattern recognition (2017)

400 Citations

On Human Motion Prediction Using Recurrent Neural Networks

Julieta Martinez;Michael J. Black;Javier Romero.
computer vision and pattern recognition (2017)

387 Citations

Embodied hands: modeling and capturing hands and bodies together

Javier Romero;Dimitrios Tzionas;Michael J. Black.
ACM Transactions on Graphics (2017)

377 Citations

Dyna: a model of dynamic human shape in motion

Gerard Pons-Moll;Javier Romero;Naureen Mahmood;Michael J. Black.
international conference on computer graphics and interactive techniques (2015)

274 Citations

Visual object-action recognition: Inferring object affordances from human demonstration

Hedvig Kjellström;Javier Romero;Danica Kragić.
Computer Vision and Image Understanding (2011)

256 Citations

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Best Scientists Citing Javier Romero

Christian Theobalt

Christian Theobalt

Max Planck Institute for Informatics

Publications: 114

Michael J. Black

Michael J. Black

Max Planck Institute for Intelligent Systems

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Gerard Pons-Moll

Gerard Pons-Moll

University of Tübingen

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Pascal Fua

Pascal Fua

École Polytechnique Fédérale de Lausanne

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Michael M. Bronstein

Michael M. Bronstein

Imperial College London

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Emanuele Rodolà

Emanuele Rodolà

Sapienza University of Rome

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Juergen Gall

Juergen Gall

University of Bonn

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Mathieu Salzmann

Mathieu Salzmann

École Polytechnique Fédérale de Lausanne

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Maks Ovsjanikov

Maks Ovsjanikov

École Polytechnique

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Otmar Hilliges

Otmar Hilliges

ETH Zurich

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Danica Kragic

Danica Kragic

Royal Institute of Technology

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Daniel Cremers

Daniel Cremers

Technical University of Munich

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Yu-Kun Lai

Yu-Kun Lai

Cardiff University

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Stefanos Zafeiriou

Stefanos Zafeiriou

Imperial College London

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Junsong Yuan

Junsong Yuan

University at Buffalo, State University of New York

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Hao Li

Hao Li

University of California, Berkeley

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