World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
7412
World Ranking
11961
National Ranking
4885

Overview

Vladimir G. Kim is affiliated with Adobe Systems in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a strong focus on subfields such as Computational Mechanics, Computer Vision and Pattern Recognition, and Computer Graphics and Computer-Aided Design.

The scientist's body of work covers a range of topics including 3D Shape Modeling and Analysis, Computer Graphics and Visualization Techniques, Advanced Numerical Analysis Techniques, Image Processing and 3D Reconstruction, Manufacturing Process and Optimization, Advanced Vision and Imaging, and Human Pose and Action Recognition.

Recent publications highlight their contributions to computer graphics and vision conferences and journals. Selected papers include:

  • "AutoMate," 2021, ACM Transactions on Graphics
  • "Neural subdivision," 2020, ACM Transactions on Graphics
  • "Neural jacobian fields," 2022, ACM Transactions on Graphics
  • "GLASS: Geometric Latent Augmentation for Shape Spaces," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Neural Convolutional Surfaces," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Vladimir G. Kim frequently collaborates with several coauthors, including Noam Aigerman, Siddhartha Chaudhuri, Matthew Fisher, Niloy J. Mitra, and Adriana Schulz.

The venues in which the scientist often publishes include:

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

Best Publications

  • A scalable active framework for region annotation in 3D shape collections

    Li Yi;Vladimir G. Kim;Duygu Ceylan;I-Chao Shen

  • A Papier-Mâché Approach to Learning 3D Surface Generation

    Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell

  • A Papier-Mache Approach to Learning 3D Surface Generation

    Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell

  • Shape-based recognition of 3D point clouds in urban environments

    Aleksey Golovinskiy;Vladimir G. Kim;Thomas Funkhouser

  • Blended intrinsic maps

    Vladimir G. Kim;Yaron Lipman;Thomas Funkhouser

  • AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

    Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell

  • Multi-content GAN for Few-Shot Font Style Transfer

    Samaneh Azadi;Matthew Fisher;Vladimir Kim;Zhaowen Wang

  • 3D-CODED: 3D Correspondences by Deep Deformation

    Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell

  • Blended intrinsic maps

    Unknown

  • Learning part-based templates from large collections of 3D shapes

    Vladimir G. Kim;Wilmot Li;Niloy J. Mitra;Siddhartha Chaudhuri

  • Convolutional neural networks on surfaces via seamless toric covers

    Haggai Maron;Meirav Galun;Noam Aigerman;Miri Trope

  • Structure-aware shape processing

    Niloy J. Mitra;Michael Wand;Hao Zhang;Daniel Cohen-Or

  • Exploring collections of 3D models using fuzzy correspondences

    Vladimir G. Kim;Wilmot Li;Niloy J. Mitra;Stephen DiVerdi

  • Entropic metric alignment for correspondence problems

    Justin Solomon;Gabriel Peyré;Vladimir G. Kim;Suvrit Sra

  • Data-driven structural priors for shape completion

    Minhyuk Sung;Vladimir G. Kim;Roland Angst;Leonidas Guibas

  • Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks

    Haibin Huang;Evangelos Kalogerakis;Siddhartha Chaudhuri;Duygu Ceylan

  • Data-driven shape analysis and processing

    Kai Xu;Vladimir G. Kim;Qixing Huang;Niloy Mitra

  • Shape2Pose: human-centric shape analysis

    Vladimir G. Kim;Siddhartha Chaudhuri;Leonidas Guibas;Thomas Funkhouser

  • Rank algorithms for picture processing

    Unknown

  • Learning elementary structures for 3D shape generation and matching

    Theo Deprelle;Thibault Groueix;Matthew Fisher;Vladimir G. Kim

  • Neural Cages for Detail-Preserving 3D Deformations

    Wang Yifan;Noam Aigerman;Vladimir G. Kim;Siddhartha Chaudhuri

  • Physics-driven pattern adjustment for direct 3D garment editing

    Aric Bartle;Alla Sheffer;Vladimir G. Kim;Danny M. Kaufman

  • Möbius Transformations For Global Intrinsic Symmetry Analysis

    Vladimir G. Kim;Yaron Lipman;Xiaobai Chen;Thomas Allen Funkhouser

  • AtlasNet: A Papier-M\^ach'e Approach to Learning 3D Surface Generation

    Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell

  • Data-Driven Shape Analysis and Processing

    Kai Xu;Vladimir G. Kim;Qixing Huang;Evangelos Kalogerakis

Frequent Co-Authors

Bryan C. Russell
Bryan C. Russell Adobe Systems (United States)
Niloy J. Mitra
Niloy J. Mitra University College London
Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Ersin Yumer
Ersin Yumer Adobe Systems (United States)
Thomas Funkhouser
Thomas Funkhouser Google (United States)
Eli Shechtman
Eli Shechtman Adobe Systems (United States)
Evangelos Kalogerakis
Evangelos Kalogerakis Technical University of Crete
Hao Su
Hao Su University of California, San Diego
Qixing Huang
Qixing Huang The University of Texas at Austin
Yaron Lipman
Yaron Lipman Facebook (United States)

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