Jun Saito

2.2k citations
19 papers · 1.4k · 2 hit papers · h-index 10

Impact in

Papers in

Jun Saito

18 papers receiving 1.3k citations

Jun Saito's Hit Papers

Phase-functioned neural networks for character control 2017 · 328 citations
3280+3+6Years since publication100200300

Peers

Jun Saito
Comparison fields: 5 of 75
  • Computer Vision and Pattern Recognition 1.1k
  • Control and Systems Engineering 1.0k
  • Human-Computer Interaction 114
  • Computer Graphics and Computer-Aided Design 67
  • Computational Mechanics 206
Replace Alla Safonova with:
Alla Safonova United States
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Hanbyul Joo South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Jun Saito

Since Specialization
Citations

This map shows the geographic impact of Jun Saito's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Saito with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Saito more than expected).

Fields of papers citing papers by Jun Saito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Saito. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Saito. The network helps show where Jun Saito may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Saito, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Saito Line = papers co-authored together Jun Saito links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1
A deep learning framework for character motion synthesis and editing
Hit paper breakdown →
2016393
2
Phase-functioned neural networks for character control
Hit paper breakdown →
2017328
3 2019178
4 2015176
5 2018165
6 202222
7 201521
8 202214
9 201310
10 201610
11 20239
12 20168
13 20175
14 20194
15 20053
16 19972
17 20212
18 20241
19 19951

About Jun Saito

Jun Saito is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Computational Mechanics, Mechanical Engineering and Civil and Structural Engineering, having authored 19 papers that have together received 1.4k indexed citations. Recurring topics across this work include Human Motion and Animation (10 papers), Human Pose and Action Recognition (8 papers), 3D Shape Modeling and Analysis (6 papers), Video Analysis and Summarization (6 papers), Face recognition and analysis (2 papers), Interactive and Immersive Displays (2 papers), Teleoperation and Haptic Systems (2 papers) and Computer Graphics and Visualization Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Control and Systems Engineering (1.0k citations), Human-Computer Interaction (114 citations), Computer Graphics and Computer-Aided Design (67 citations) and Computational Mechanics (206 citations). Jun Saito has collaborated with scholars based in United Kingdom, United States and Japan. Frequent co-authors include Taku Komura, Daniel Holden, Sebastian Starke, He Zhang, T. A. Joyce, He Zhang, Noam Aigerman, Thibault Groueix, Vladimir G. Kim and Siddhartha Chaudhuri. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics, Journal of Robotics and Mechatronics, AIJ Journal of Technology and Design and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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